{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    }
   ],
   "source": [
    "import arviz as az\n",
    "import numpy as np  # For vectorized math operations\n",
    "import pandas as pd  # For file input/output\n",
    "import pymc as pm\n",
    "import pytensor.tensor as pt\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.lines import Line2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'  # high resolution figures\n",
    "az.style.use(\"arviz-darkgrid\")\n",
    "plt.rcParams[\"font.family\"] = \"Latin Modern Roman\"\n",
    "\n",
    "rng = np.random.default_rng(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use a standard dataset for this example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idcase</th>\n",
       "      <th>depvar</th>\n",
       "      <th>ic.gc</th>\n",
       "      <th>ic.gr</th>\n",
       "      <th>ic.ec</th>\n",
       "      <th>ic.er</th>\n",
       "      <th>ic.hp</th>\n",
       "      <th>oc.gc</th>\n",
       "      <th>oc.gr</th>\n",
       "      <th>oc.ec</th>\n",
       "      <th>oc.er</th>\n",
       "      <th>oc.hp</th>\n",
       "      <th>income</th>\n",
       "      <th>agehed</th>\n",
       "      <th>rooms</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>gc</td>\n",
       "      <td>866.0</td>\n",
       "      <td>962.64</td>\n",
       "      <td>859.9</td>\n",
       "      <td>995.76</td>\n",
       "      <td>1135.5</td>\n",
       "      <td>199.69</td>\n",
       "      <td>151.72</td>\n",
       "      <td>553.34</td>\n",
       "      <td>505.6</td>\n",
       "      <td>237.88</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>ncostl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   idcase depvar  ic.gc   ic.gr  ic.ec   ic.er   ic.hp   oc.gc   oc.gr  \\\n",
       "0       1     gc  866.0  962.64  859.9  995.76  1135.5  199.69  151.72   \n",
       "\n",
       "    oc.ec  oc.er   oc.hp  income  agehed  rooms  region  \n",
       "0  553.34  505.6  237.88       7      25      6  ncostl  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    wide_heating_df = pd.read_csv(\"../data/heating_data_r.csv\")\n",
    "except:\n",
    "    wide_heating_df = pd.read_csv(pm.get_data(\"heating_data_r.csv\"))\n",
    "\n",
    "wide_heating_df[wide_heating_df[\"idcase\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idcase</th>\n",
       "      <th>alt_id</th>\n",
       "      <th>choice</th>\n",
       "      <th>depvar</th>\n",
       "      <th>income</th>\n",
       "      <th>agehed</th>\n",
       "      <th>rooms</th>\n",
       "      <th>region</th>\n",
       "      <th>installation_costs</th>\n",
       "      <th>operating_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>gc</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>ncostl</td>\n",
       "      <td>866.00</td>\n",
       "      <td>199.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>gc</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>ncostl</td>\n",
       "      <td>962.64</td>\n",
       "      <td>151.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>gc</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>ncostl</td>\n",
       "      <td>859.90</td>\n",
       "      <td>553.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>gc</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>ncostl</td>\n",
       "      <td>995.76</td>\n",
       "      <td>505.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>gc</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>ncostl</td>\n",
       "      <td>1135.50</td>\n",
       "      <td>237.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   idcase  alt_id  choice depvar  income  agehed  rooms  region  \\\n",
       "0       1       1       1     gc       7      25      6  ncostl   \n",
       "1       1       2       0     gc       7      25      6  ncostl   \n",
       "2       1       3       0     gc       7      25      6  ncostl   \n",
       "3       1       4       0     gc       7      25      6  ncostl   \n",
       "4       1       5       0     gc       7      25      6  ncostl   \n",
       "\n",
       "   installation_costs  operating_costs  \n",
       "0              866.00           199.69  \n",
       "1              962.64           151.72  \n",
       "2              859.90           553.34  \n",
       "3              995.76           505.60  \n",
       "4             1135.50           237.88  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivoted data that is more amenable to analysis\n",
    " \n",
    "try:\n",
    "    long_heating_df = pd.read_csv(\"../data/long_heating_data.csv\")\n",
    "except:\n",
    "    long_heating_df = pd.read_csv(pm.get_data(\"long_heating_data.csv\"))\n",
    "\n",
    "columns = [c for c in long_heating_df.columns if c != \"Unnamed: 0\"]\n",
    "long_heating_df[long_heating_df[\"idcase\"] == 1][columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [beta_ic, beta_oc, y_cat]\n"
     ]
    },
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       "  0%|          | 0/2000 [00:00<?, ?it/s]"
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    {
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       "  0%|          | 0/2000 [00:00<?, ?it/s]"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y_cat]\n"
     ]
    },
    {
     "data": {
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       "model_id": "2771e2847ce141f1917c1f9a0a63867b",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "N = wide_heating_df.shape[0]\n",
    "observed = pd.Categorical(wide_heating_df[\"depvar\"]).codes\n",
    "coords = {\n",
    "    \"alts_probs\": [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
    "    \"obs\": range(N),\n",
    "}\n",
    "\n",
    "with pm.Model(coords=coords) as model_1:\n",
    "    beta_ic = pm.Normal(\"beta_ic\", 0, 1)\n",
    "    beta_oc = pm.Normal(\"beta_oc\", 0, 1)\n",
    "\n",
    "    ## Construct Utility matrix and Pivot\n",
    "    u0 = beta_ic * wide_heating_df[\"ic.ec\"] + beta_oc * wide_heating_df[\"oc.ec\"]\n",
    "    u1 = beta_ic * wide_heating_df[\"ic.er\"] + beta_oc * wide_heating_df[\"oc.er\"]\n",
    "    u2 = beta_ic * wide_heating_df[\"ic.gc\"] + beta_oc * wide_heating_df[\"oc.gc\"]\n",
    "    u3 = beta_ic * wide_heating_df[\"ic.gr\"] + beta_oc * wide_heating_df[\"oc.gr\"]\n",
    "    u4 = np.zeros(N)  # Outside Good\n",
    "    s = pm.math.stack([u0, u1, u2, u3, u4]).T\n",
    "\n",
    "    ## Apply Softmax Transform\n",
    "    p_ = pm.Deterministic(\"p\", pm.math.softmax(s, axis=1), dims=(\"obs\", \"alts_probs\"))\n",
    "\n",
    "    ## Likelihood\n",
    "    choice_obs = pm.Categorical(\"y_cat\", p=p_, observed=observed, dims=\"obs\")\n",
    "\n",
    "    idata_m1 = pm.sample_prior_predictive()\n",
    "    idata_m1.extend(\n",
    "        pm.sample(nuts_sampler=\"numpyro\", idata_kwargs={\"log_likelihood\": True}, random_seed=101)\n",
    "    )\n",
    "    idata_m1.extend(pm.sample_posterior_predictive(idata_m1))\n",
    "\n",
    "pm.model_to_graphviz(model_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "\n",
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       ".xr-var-data,\n",
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       ".xr-index-data,\n",
       ".xr-attrs {\n",
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       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
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       "\n",
       "dl.xr-attrs {\n",
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       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 144MB\n",
       "Dimensions:     (chain: 4, draw: 1000, obs: 900, alts_probs: 5)\n",
       "Coordinates:\n",
       "  * chain       (chain) int64 32B 0 1 2 3\n",
       "  * draw        (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * obs         (obs) int64 7kB 0 1 2 3 4 5 6 7 ... 893 894 895 896 897 898 899\n",
       "  * alts_probs  (alts_probs) &lt;U2 40B &#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;\n",
       "Data variables:\n",
       "    beta_ic     (chain, draw) float64 32kB 0.001832 0.001817 ... 0.001948\n",
       "    beta_oc     (chain, draw) float64 32kB -0.003705 -0.003709 ... -0.004581\n",
       "    p           (chain, draw, obs, alts_probs) float64 144MB 0.07556 ... 0.1102\n",
       "Attributes:\n",
       "    created_at:     2024-06-23T20:32:15.207517+00:00\n",
       "    arviz_version:  0.18.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-85a050a5-91cd-4fc6-bd4b-7d094451c3a3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-85a050a5-91cd-4fc6-bd4b-7d094451c3a3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>obs</span>: 900</li><li><span class='xr-has-index'>alts_probs</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ea9975be-834e-4171-b402-4b78ee8f4998' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ea9975be-834e-4171-b402-4b78ee8f4998' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-3a2eb6a8-9220-464f-a2d6-fee4e091f591' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a2eb6a8-9220-464f-a2d6-fee4e091f591' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c7cd0c6-5a53-44b7-b637-eccd164789b2' class='xr-var-data-in' type='checkbox'><label for='data-0c7cd0c6-5a53-44b7-b637-eccd164789b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-ce83fce1-f177-4a60-af6c-a3c84fc58e61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce83fce1-f177-4a60-af6c-a3c84fc58e61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-518d6aa3-1b6a-48cf-ae36-5a4175486469' class='xr-var-data-in' type='checkbox'><label for='data-518d6aa3-1b6a-48cf-ae36-5a4175486469' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-653c79ca-1515-4c03-9543-4675f8b2c2f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-653c79ca-1515-4c03-9543-4675f8b2c2f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8dfa46c-23ab-4d12-a488-abe81866b639' class='xr-var-data-in' type='checkbox'><label for='data-e8dfa46c-23ab-4d12-a488-abe81866b639' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alts_probs</span></div><div class='xr-var-dims'>(alts_probs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</div><input id='attrs-e44b1aa0-ebb5-47fc-b5f5-d72a336479ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e44b1aa0-ebb5-47fc-b5f5-d72a336479ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-032f60af-daf6-42ff-a305-92ef20cba823' class='xr-var-data-in' type='checkbox'><label for='data-032f60af-daf6-42ff-a305-92ef20cba823' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d3c5515-537b-49ca-8e3e-bd237768c474' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d3c5515-537b-49ca-8e3e-bd237768c474' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta_ic</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001832 0.001817 ... 0.001948</div><input id='attrs-a714dbc8-63f6-4145-9197-7a073bf1b552' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a714dbc8-63f6-4145-9197-7a073bf1b552' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8957d6fe-47fe-4b7c-baae-949f41bf2750' class='xr-var-data-in' type='checkbox'><label for='data-8957d6fe-47fe-4b7c-baae-949f41bf2750' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00183239, 0.00181733, 0.00180854, ..., 0.00181192, 0.00175778,\n",
       "        0.00210304],\n",
       "       [0.00210654, 0.00210654, 0.00197494, ..., 0.0019049 , 0.00187513,\n",
       "        0.00181919],\n",
       "       [0.00181312, 0.00179163, 0.00173467, ..., 0.002031  , 0.00212423,\n",
       "        0.00171305],\n",
       "       [0.00185484, 0.00177189, 0.00187684, ..., 0.00206325, 0.00207469,\n",
       "        0.00194822]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_oc</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.003705 -0.003709 ... -0.004581</div><input id='attrs-9d06873e-3e4c-42a5-af05-5eb5c31ad4c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d06873e-3e4c-42a5-af05-5eb5c31ad4c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24f885a7-7223-4965-aaa6-aac08e86c69e' class='xr-var-data-in' type='checkbox'><label for='data-24f885a7-7223-4965-aaa6-aac08e86c69e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.00370539, -0.00370935, -0.00431479, ..., -0.00461288,\n",
       "        -0.00460595, -0.00406624],\n",
       "       [-0.00443282, -0.00443282, -0.00414015, ..., -0.00426425,\n",
       "        -0.00412309, -0.0040975 ],\n",
       "       [-0.00395141, -0.00370944, -0.00377519, ..., -0.0049641 ,\n",
       "        -0.00476948, -0.0036491 ],\n",
       "       [-0.00381868, -0.00404807, -0.00393956, ..., -0.0042988 ,\n",
       "        -0.00462425, -0.00458066]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(chain, draw, obs, alts_probs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.07556 0.1157 ... 0.428 0.1102</div><input id='attrs-a914e6b4-360c-4424-be5a-a34d0b5bf114' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a914e6b4-360c-4424-be5a-a34d0b5bf114' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ff27507-3f75-48ab-8bae-38a15972fed4' class='xr-var-data-in' type='checkbox'><label for='data-6ff27507-3f75-48ab-8bae-38a15972fed4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[0.07556231, 0.115677  , 0.28331138, 0.4039845 , 0.1214648 ],\n",
       "         [0.09409705, 0.12757696, 0.30515943, 0.32297186, 0.15019469],\n",
       "         [0.10440213, 0.16496078, 0.23067664, 0.3579255 , 0.14203495],\n",
       "         ...,\n",
       "         [0.08474306, 0.13638204, 0.23587751, 0.43633533, 0.10666206],\n",
       "         [0.07042229, 0.18021517, 0.22561794, 0.42184934, 0.10189526],\n",
       "         [0.08717172, 0.13668048, 0.27121101, 0.40382201, 0.10111478]],\n",
       "\n",
       "        [[0.07540941, 0.11522867, 0.28310845, 0.40318444, 0.12306902],\n",
       "         [0.09378437, 0.12698266, 0.30488553, 0.32253151, 0.15181593],\n",
       "         [0.1041803 , 0.16418621, 0.23085429, 0.35725122, 0.14352798],\n",
       "         ...,\n",
       "         [0.08467137, 0.13587604, 0.23615981, 0.43515429, 0.10813849],\n",
       "         [0.07046016, 0.17950217, 0.22578659, 0.42086267, 0.10338842],\n",
       "         [0.08701837, 0.13621822, 0.27140404, 0.40272926, 0.10263011]],\n",
       "\n",
       "        [[0.06122794, 0.0961875 , 0.28473604, 0.41709752, 0.140751  ],\n",
       "         [0.07754801, 0.10707428, 0.3120934 , 0.3300667 , 0.17321761],\n",
       "         [0.08971942, 0.14413631, 0.23485909, 0.36901728, 0.1622679 ],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [0.07148748, 0.12262564, 0.23524756, 0.47222741, 0.09841191],\n",
       "         [0.05809065, 0.1693353 , 0.22340738, 0.45541326, 0.09375342],\n",
       "         [0.07380963, 0.12341259, 0.27669202, 0.43293536, 0.0931504 ]],\n",
       "\n",
       "        [[0.0557185 , 0.09210605, 0.28955146, 0.44171102, 0.12091297],\n",
       "         [0.07321771, 0.10485256, 0.32256519, 0.34396418, 0.15540036],\n",
       "         [0.08500087, 0.14479629, 0.23452216, 0.39027898, 0.14540171],\n",
       "         ...,\n",
       "         [0.06514664, 0.11311106, 0.2365109 , 0.48142957, 0.10380182],\n",
       "         [0.05287363, 0.15948594, 0.22320083, 0.4650963 , 0.09934329],\n",
       "         [0.0669652 , 0.11434375, 0.28043246, 0.43930699, 0.09895161]],\n",
       "\n",
       "        [[0.05622438, 0.09116917, 0.28748885, 0.43233235, 0.13278524],\n",
       "         [0.07282079, 0.10284997, 0.31870176, 0.33851648, 0.16711101],\n",
       "         [0.08500239, 0.14132398, 0.2353021 , 0.38212832, 0.15624321],\n",
       "         ...,\n",
       "         [0.06612004, 0.11180751, 0.23856091, 0.46873109, 0.11478046],\n",
       "         [0.05429461, 0.15656816, 0.22464392, 0.4540332 , 0.11046011],\n",
       "         [0.06750051, 0.11316997, 0.28109683, 0.4280232 , 0.11020948]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a005acf9-35b8-4ac2-af6f-bfbf49c293c6' class='xr-section-summary-in' type='checkbox'  ><label for='section-a005acf9-35b8-4ac2-af6f-bfbf49c293c6' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f3d1e6c6-4a0f-4016-9ed6-77900d21d340' class='xr-index-data-in' type='checkbox'/><label for='index-f3d1e6c6-4a0f-4016-9ed6-77900d21d340' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4dfa51c9-b9d7-453a-8194-4eb0afac6c82' class='xr-index-data-in' type='checkbox'/><label for='index-4dfa51c9-b9d7-453a-8194-4eb0afac6c82' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2faab5d2-aceb-4062-9e38-66afcfb38d67' class='xr-index-data-in' type='checkbox'/><label for='index-2faab5d2-aceb-4062-9e38-66afcfb38d67' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>alts_probs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2d642994-4ef2-483a-b178-bd4d85d7bf0e' class='xr-index-data-in' type='checkbox'/><label for='index-2d642994-4ef2-483a-b178-bd4d85d7bf0e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;alts_probs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9123d0bc-ebc1-42e9-b8b2-90ae8a3ca233' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9123d0bc-ebc1-42e9-b8b2-90ae8a3ca233' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-23T20:32:15.207517+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_posterior_predictivec32b75d9-f904-4605-8a20-80681f1e9823\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_posterior_predictivec32b75d9-f904-4605-8a20-80681f1e9823\" class = \"xr-section-summary\">posterior_predictive</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
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       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 29MB\n",
       "Dimensions:  (chain: 4, draw: 1000, obs: 900)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 32B 0 1 2 3\n",
       "  * draw     (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * obs      (obs) int64 7kB 0 1 2 3 4 5 6 7 ... 892 893 894 895 896 897 898 899\n",
       "Data variables:\n",
       "    y_cat    (chain, draw, obs) int64 29MB 3 3 2 3 3 4 3 2 2 ... 3 3 3 2 3 3 4 1\n",
       "Attributes:\n",
       "    created_at:                 2024-06-23T20:32:21.823805+00:00\n",
       "    arviz_version:              0.18.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.15.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-88ffcd69-bf6a-4db2-ba4c-d4d02c08ff3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-88ffcd69-bf6a-4db2-ba4c-d4d02c08ff3a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>obs</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-819d04f3-092c-451c-8318-fc4cc577908d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-819d04f3-092c-451c-8318-fc4cc577908d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-2d4feda5-25d3-4c53-b99c-6bd496641202' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d4feda5-25d3-4c53-b99c-6bd496641202' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70fadbe6-b3e2-4320-b369-4d8d85752788' class='xr-var-data-in' type='checkbox'><label for='data-70fadbe6-b3e2-4320-b369-4d8d85752788' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-fb444e25-9570-47f6-97d0-06bc4729e33d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb444e25-9570-47f6-97d0-06bc4729e33d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9a8bb51-0b83-4a93-8c66-c2c9babcd5ea' class='xr-var-data-in' type='checkbox'><label for='data-c9a8bb51-0b83-4a93-8c66-c2c9babcd5ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-c0a79315-4a4c-48fc-b872-1dcf5df7f1ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0a79315-4a4c-48fc-b872-1dcf5df7f1ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf21610c-3ea1-43a6-bcee-96fc3fb4cc3e' class='xr-var-data-in' type='checkbox'><label for='data-bf21610c-3ea1-43a6-bcee-96fc3fb4cc3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b455972-451b-4df2-99fe-a2b00888c8e1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5b455972-451b-4df2-99fe-a2b00888c8e1' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(chain, draw, obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 3 2 3 3 4 3 2 ... 3 3 3 2 3 3 4 1</div><input id='attrs-8d80d723-7e6f-4c54-8c8b-341b9e4972e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d80d723-7e6f-4c54-8c8b-341b9e4972e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f65a9212-00f4-4196-b601-869505a2295d' class='xr-var-data-in' type='checkbox'><label for='data-f65a9212-00f4-4196-b601-869505a2295d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[3, 3, 2, ..., 2, 0, 3],\n",
       "        [3, 2, 1, ..., 3, 4, 4],\n",
       "        [2, 3, 2, ..., 4, 4, 2],\n",
       "        ...,\n",
       "        [2, 3, 3, ..., 2, 1, 3],\n",
       "        [4, 2, 2, ..., 3, 1, 2],\n",
       "        [1, 2, 2, ..., 2, 3, 3]],\n",
       "\n",
       "       [[2, 4, 2, ..., 2, 2, 3],\n",
       "        [4, 3, 3, ..., 3, 3, 3],\n",
       "        [2, 3, 2, ..., 3, 3, 2],\n",
       "        ...,\n",
       "        [3, 2, 0, ..., 0, 0, 2],\n",
       "        [2, 3, 1, ..., 2, 1, 2],\n",
       "        [2, 2, 0, ..., 2, 2, 2]],\n",
       "\n",
       "       [[4, 2, 2, ..., 1, 3, 3],\n",
       "        [3, 3, 3, ..., 4, 2, 2],\n",
       "        [3, 3, 3, ..., 2, 3, 3],\n",
       "        ...,\n",
       "        [3, 2, 0, ..., 0, 3, 1],\n",
       "        [2, 1, 2, ..., 2, 3, 2],\n",
       "        [3, 2, 3, ..., 1, 3, 3]],\n",
       "\n",
       "       [[4, 3, 3, ..., 2, 3, 3],\n",
       "        [2, 4, 3, ..., 4, 1, 2],\n",
       "        [4, 3, 3, ..., 3, 3, 2],\n",
       "        ...,\n",
       "        [3, 3, 3, ..., 1, 2, 4],\n",
       "        [4, 3, 3, ..., 3, 0, 3],\n",
       "        [3, 1, 3, ..., 3, 4, 1]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23102847-b1e0-4b1d-b73c-8cbe10c644b1' class='xr-section-summary-in' type='checkbox'  ><label for='section-23102847-b1e0-4b1d-b73c-8cbe10c644b1' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c1c616df-009e-4cc3-a7c1-75103dda60c9' class='xr-index-data-in' type='checkbox'/><label for='index-c1c616df-009e-4cc3-a7c1-75103dda60c9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f10dd357-88c4-4af2-b998-e9139bbbf420' class='xr-index-data-in' type='checkbox'/><label for='index-f10dd357-88c4-4af2-b998-e9139bbbf420' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7acfdef8-3fa4-4cea-b65d-a0d33c900dab' class='xr-index-data-in' type='checkbox'/><label for='index-7acfdef8-3fa4-4cea-b65d-a0d33c900dab' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-399b2c80-080e-4d41-8d16-c140f835d956' class='xr-section-summary-in' type='checkbox'  checked><label for='section-399b2c80-080e-4d41-8d16-c140f835d956' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-23T20:32:21.823805+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.15.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
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       "            \n",
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       "                  <input id=\"idata_log_likelihoodc794511f-552a-4a40-a444-2c57a4091fe0\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
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       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "\n",
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       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-details {\n",
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       "\n",
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       "\n",
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       "  grid-column: 1;\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
       ".xr-has-index {\n",
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       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-var-item > div,\n",
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       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 29MB\n",
       "Dimensions:      (chain: 4, draw: 1000, y_cat_dim_0: 1, y_cat_dim_1: 900)\n",
       "Coordinates:\n",
       "  * chain        (chain) int64 32B 0 1 2 3\n",
       "  * draw         (draw) int64 8kB 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "  * y_cat_dim_0  (y_cat_dim_0) int64 8B 0\n",
       "  * y_cat_dim_1  (y_cat_dim_1) int64 7kB 0 1 2 3 4 5 ... 894 895 896 897 898 899\n",
       "Data variables:\n",
       "    y_cat        (chain, draw, y_cat_dim_0, y_cat_dim_1) float64 29MB -1.261 ...\n",
       "Attributes:\n",
       "    created_at:     2024-06-23T20:32:15.210389+00:00\n",
       "    arviz_version:  0.18.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a0a7c637-7a04-4d06-87bf-e9c7db7ccb82' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a0a7c637-7a04-4d06-87bf-e9c7db7ccb82' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>y_cat_dim_0</span>: 1</li><li><span class='xr-has-index'>y_cat_dim_1</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c3be4dfd-724e-4d96-bd43-9b5fc53590df' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c3be4dfd-724e-4d96-bd43-9b5fc53590df' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-e4e14eb9-c8b0-4b47-bbc5-54a3df73adc8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4e14eb9-c8b0-4b47-bbc5-54a3df73adc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7de90388-6ed5-464f-bfad-b9892b9648c0' class='xr-var-data-in' type='checkbox'><label for='data-7de90388-6ed5-464f-bfad-b9892b9648c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-ddc5328e-6aa6-43f8-b4b6-88877c217f01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ddc5328e-6aa6-43f8-b4b6-88877c217f01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d64bb1d-5c70-4eed-a998-ec23cefe23d8' class='xr-var-data-in' type='checkbox'><label for='data-1d64bb1d-5c70-4eed-a998-ec23cefe23d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_cat_dim_0</span></div><div class='xr-var-dims'>(y_cat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-2b888020-08e5-49b0-be37-82a8fccae480' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b888020-08e5-49b0-be37-82a8fccae480' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0df51ac7-ac05-4cfb-bb47-a7caee80a85a' class='xr-var-data-in' type='checkbox'><label for='data-0df51ac7-ac05-4cfb-bb47-a7caee80a85a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_cat_dim_1</span></div><div class='xr-var-dims'>(y_cat_dim_1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-2c2cc589-370f-420c-83da-643380c3eb53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c2cc589-370f-420c-83da-643380c3eb53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6489e3c7-8f4d-44d5-b7d7-00fa3c8b1b3b' class='xr-var-data-in' type='checkbox'><label for='data-6489e3c7-8f4d-44d5-b7d7-00fa3c8b1b3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ba81286-89a3-4795-abb6-21e789d26280' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ba81286-89a3-4795-abb6-21e789d26280' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(chain, draw, y_cat_dim_0, y_cat_dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.261 -1.187 ... -1.493 -1.269</div><input id='attrs-20ffb09c-817c-484d-8e4e-46f8834b2083' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20ffb09c-817c-484d-8e4e-46f8834b2083' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fba811f2-d363-48ea-9dff-ecaa747f32ad' class='xr-var-data-in' type='checkbox'><label for='data-fba811f2-d363-48ea-9dff-ecaa747f32ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[-1.2612087 , -1.18692092, -1.46673836, ..., -1.44444265,\n",
       "          -1.48891222, -1.30485813]],\n",
       "\n",
       "        [[-1.26192523, -1.18781889, -1.46596855, ..., -1.44324653,\n",
       "          -1.48816503, -1.30414666]],\n",
       "\n",
       "        [[-1.25619272, -1.16445278, -1.44876958, ..., -1.42865775,\n",
       "          -1.48677005, -1.27635524]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-1.25606194, -1.15560848, -1.44314675, ..., -1.42458852,\n",
       "          -1.48873712, -1.26557903]],\n",
       "\n",
       "        [[-1.26036161, -1.16141909, -1.44246931, ..., -1.4220514 ,\n",
       "          -1.48744086, -1.26553536]],\n",
       "\n",
       "        [[-1.2432454 , -1.15168259, -1.46743232, ..., -1.45639165,\n",
       "          -1.50228702, -1.29804564]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[-1.25808289, -1.1799529 , -1.46386658, ..., -1.4428013 ,\n",
       "          -1.4892258 , -1.2997839 ]],\n",
       "\n",
       "        [[-1.26018275, -1.17702843, -1.45418793, ..., -1.43166875,\n",
       "          -1.48506219, -1.2867652 ]],\n",
       "\n",
       "        [[-1.25512316, -1.17290448, -1.4609046 , ..., -1.44114666,\n",
       "          -1.48966503, -1.29452733]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-1.24190813, -1.14419259, -1.45813717, ..., -1.44711688,\n",
       "          -1.49875835, -1.28485022]],\n",
       "\n",
       "        [[-1.23942224, -1.13145002, -1.45020518, ..., -1.441761  ,\n",
       "          -1.49968331, -1.27142236]],\n",
       "\n",
       "        [[-1.2465712 , -1.14349955, -1.44688504, ..., -1.43313061,\n",
       "          -1.49323869, -1.26905606]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-50aaba3d-df00-47d8-a3bf-dc92ed15ff94' class='xr-section-summary-in' type='checkbox'  ><label for='section-50aaba3d-df00-47d8-a3bf-dc92ed15ff94' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fa8d4be9-d9c0-4ee6-b561-dde1a550f337' class='xr-index-data-in' type='checkbox'/><label for='index-fa8d4be9-d9c0-4ee6-b561-dde1a550f337' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ce41c33b-7180-4ef5-abe4-3aa93ccfc4e4' class='xr-index-data-in' type='checkbox'/><label for='index-ce41c33b-7180-4ef5-abe4-3aa93ccfc4e4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_cat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ac7e7e79-1a54-4e6a-a11a-918c0bfdccc3' class='xr-index-data-in' type='checkbox'/><label for='index-ac7e7e79-1a54-4e6a-a11a-918c0bfdccc3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;y_cat_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_cat_dim_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5a2c14f3-b4b5-4543-b3e6-8772d35d1a58' class='xr-index-data-in' type='checkbox'/><label for='index-5a2c14f3-b4b5-4543-b3e6-8772d35d1a58' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;y_cat_dim_1&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a3ab061-699c-411a-aa7d-1357c6bec832' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a3ab061-699c-411a-aa7d-1357c6bec832' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-23T20:32:15.210389+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats967950e4-c38d-4aeb-b004-9833104fdca6\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats967950e4-c38d-4aeb-b004-9833104fdca6\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 204kB\n",
       "Dimensions:          (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 32B 0 1 2 3\n",
       "  * draw             (draw) int64 8kB 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    acceptance_rate  (chain, draw) float64 32kB 0.9628 1.0 ... 0.9096 0.8808\n",
       "    diverging        (chain, draw) bool 4kB False False False ... False False\n",
       "    energy           (chain, draw) float64 32kB 1.312e+03 ... 1.312e+03\n",
       "    lp               (chain, draw) float64 32kB 1.311e+03 ... 1.311e+03\n",
       "    n_steps          (chain, draw) int64 32kB 3 1 7 1 3 7 3 7 ... 7 3 3 3 3 7 3\n",
       "    step_size        (chain, draw) float64 32kB 0.03082 0.03082 ... 0.0351\n",
       "    tree_depth       (chain, draw) int64 32kB 2 1 3 1 2 3 2 3 ... 3 2 2 2 2 3 2\n",
       "Attributes:\n",
       "    created_at:     2024-06-23T20:32:15.209472+00:00\n",
       "    arviz_version:  0.18.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b9102c23-45b3-4e01-bd21-9167b461146c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b9102c23-45b3-4e01-bd21-9167b461146c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-512980bd-d8b3-4160-bdd4-43ea615dbf71' class='xr-section-summary-in' type='checkbox'  checked><label for='section-512980bd-d8b3-4160-bdd4-43ea615dbf71' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-c26e2edd-1655-44f4-9090-a75e37f37598' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c26e2edd-1655-44f4-9090-a75e37f37598' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82285747-42af-4e3f-b107-3ac0955e4ceb' class='xr-var-data-in' type='checkbox'><label for='data-82285747-42af-4e3f-b107-3ac0955e4ceb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-9ef92869-08a5-4ea8-bcf4-ee28e215a5e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ef92869-08a5-4ea8-bcf4-ee28e215a5e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfc6a881-0c03-42fd-a2c4-1c0dc740c46f' class='xr-var-data-in' type='checkbox'><label for='data-dfc6a881-0c03-42fd-a2c4-1c0dc740c46f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6257cd95-3d8f-4e83-aadc-227c9be40fbe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6257cd95-3d8f-4e83-aadc-227c9be40fbe' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9628 1.0 0.9231 ... 0.9096 0.8808</div><input id='attrs-c2e3133f-709d-45de-90c6-ce8ccdf6d405' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2e3133f-709d-45de-90c6-ce8ccdf6d405' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a53f8133-7e6f-4374-9a46-d78b6c20dc53' class='xr-var-data-in' type='checkbox'><label for='data-a53f8133-7e6f-4374-9a46-d78b6c20dc53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.96283342, 1.        , 0.9230903 , ..., 0.75914853, 0.76587015,\n",
       "        0.9716857 ],\n",
       "       [0.98301043, 0.64214544, 0.71499472, ..., 0.99954378, 0.98732408,\n",
       "        0.93728333],\n",
       "       [0.97384869, 0.5090271 , 0.98637995, ..., 0.97621634, 1.        ,\n",
       "        1.        ],\n",
       "       [0.95595037, 0.9920607 , 0.95920415, ..., 0.88600583, 0.90960034,\n",
       "        0.88083658]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ea3d6356-e3f5-4fdb-9dde-e6b6c5b7072b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea3d6356-e3f5-4fdb-9dde-e6b6c5b7072b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a0bcdf2-4de8-445f-abde-9bc062a637d1' class='xr-var-data-in' type='checkbox'><label for='data-5a0bcdf2-4de8-445f-abde-9bc062a637d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.312e+03 1.311e+03 ... 1.312e+03</div><input id='attrs-43cf44b6-aaf8-4819-a08a-cf3e70bcdeae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43cf44b6-aaf8-4819-a08a-cf3e70bcdeae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50fcbf89-882e-4901-ac0f-27cfab8840ee' class='xr-var-data-in' type='checkbox'><label for='data-50fcbf89-882e-4901-ac0f-27cfab8840ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1311.62704271, 1311.40908264, 1312.02358383, ..., 1312.94174785,\n",
       "        1313.8621048 , 1314.56025091],\n",
       "       [1311.40789762, 1312.88562855, 1313.431072  , ..., 1309.88449978,\n",
       "        1310.48997299, 1310.14581203],\n",
       "       [1310.74492576, 1315.67927381, 1311.61594878, ..., 1313.55932677,\n",
       "        1313.28207282, 1311.9541163 ],\n",
       "       [1310.76018534, 1310.87052439, 1310.82664292, ..., 1312.72419099,\n",
       "        1312.61614047, 1312.07832766]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.311e+03 1.311e+03 ... 1.311e+03</div><input id='attrs-402cfd5d-5dae-4e71-909c-3d33de71fd10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-402cfd5d-5dae-4e71-909c-3d33de71fd10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-911c9808-f8c8-4891-a814-d7104e09e6b4' class='xr-var-data-in' type='checkbox'><label for='data-911c9808-f8c8-4891-a814-d7104e09e6b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1311.37579102, 1311.27521994, 1310.55444545, ..., 1312.52991085,\n",
       "        1313.54709578, 1312.22538983],\n",
       "       [1311.04717019, 1311.04717019, 1310.18177297, ..., 1309.86136009,\n",
       "        1309.84522774, 1309.9889148 ],\n",
       "       [1310.16298245, 1311.19549703, 1310.93490151, ..., 1313.3683328 ,\n",
       "        1311.97755372, 1311.60368489],\n",
       "       [1310.75790973, 1310.26335757, 1310.27104901, ..., 1310.67819214,\n",
       "        1311.08020188, 1310.81157147]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 1 7 1 3 7 3 7 ... 7 7 3 3 3 3 7 3</div><input id='attrs-1ac2fc27-5baf-468b-b9d2-c68fec990f06' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ac2fc27-5baf-468b-b9d2-c68fec990f06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09042520-3ac3-47f0-8661-6dcf01980015' class='xr-var-data-in' type='checkbox'><label for='data-09042520-3ac3-47f0-8661-6dcf01980015' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 3,  1,  7, ...,  3,  1, 15],\n",
       "       [ 3,  3,  7, ...,  7,  7,  3],\n",
       "       [15,  3,  7, ...,  3,  3, 15],\n",
       "       [ 1,  3,  3, ...,  3,  7,  3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03082 0.03082 ... 0.0351 0.0351</div><input id='attrs-0abe9701-3f6f-42e4-a3cd-056257fa5612' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0abe9701-3f6f-42e4-a3cd-056257fa5612' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df0baee7-7ae4-4648-8c82-4e24d5f78a53' class='xr-var-data-in' type='checkbox'><label for='data-df0baee7-7ae4-4648-8c82-4e24d5f78a53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.03081926, 0.03081926, 0.03081926, ..., 0.03081926, 0.03081926,\n",
       "        0.03081926],\n",
       "       [0.0387321 , 0.0387321 , 0.0387321 , ..., 0.0387321 , 0.0387321 ,\n",
       "        0.0387321 ],\n",
       "       [0.03365488, 0.03365488, 0.03365488, ..., 0.03365488, 0.03365488,\n",
       "        0.03365488],\n",
       "       [0.03509999, 0.03509999, 0.03509999, ..., 0.03509999, 0.03509999,\n",
       "        0.03509999]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 1 3 1 2 3 2 3 ... 3 3 2 2 2 2 3 2</div><input id='attrs-f409cf99-bc58-4049-b198-ad8dfdf3d045' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f409cf99-bc58-4049-b198-ad8dfdf3d045' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce288288-4d4e-4a4a-bd59-43876680e1a8' class='xr-var-data-in' type='checkbox'><label for='data-ce288288-4d4e-4a4a-bd59-43876680e1a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 1, 3, ..., 2, 1, 4],\n",
       "       [2, 2, 3, ..., 3, 3, 2],\n",
       "       [4, 2, 3, ..., 2, 2, 4],\n",
       "       [1, 2, 2, ..., 2, 3, 2]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-03180c8c-3e29-44dd-bcb8-eacc0f124772' class='xr-section-summary-in' type='checkbox'  ><label for='section-03180c8c-3e29-44dd-bcb8-eacc0f124772' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b9e813c3-db1f-48ef-9a99-0f3060a79f8c' class='xr-index-data-in' type='checkbox'/><label for='index-b9e813c3-db1f-48ef-9a99-0f3060a79f8c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a3cb72d0-7de4-43e7-84df-f0f4e1705172' class='xr-index-data-in' type='checkbox'/><label for='index-a3cb72d0-7de4-43e7-84df-f0f4e1705172' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed8d6ccc-fd1e-417c-8297-17effdafb02a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed8d6ccc-fd1e-417c-8297-17effdafb02a' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-23T20:32:15.209472+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_prior192f9abd-0be8-4869-b68f-bd06384593a2\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
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       "\n",
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       ".xr-var-data > table {\n",
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       "\n",
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       ".xr-var-data,\n",
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       ".xr-attrs {\n",
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       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
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       "\n",
       "dl.xr-attrs {\n",
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       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
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       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
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       "  stroke: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 18MB\n",
       "Dimensions:     (chain: 1, draw: 500, obs: 900, alts_probs: 5)\n",
       "Coordinates:\n",
       "  * chain       (chain) int64 8B 0\n",
       "  * draw        (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
       "  * obs         (obs) int64 7kB 0 1 2 3 4 5 6 7 ... 893 894 895 896 897 898 899\n",
       "  * alts_probs  (alts_probs) &lt;U2 40B &#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;\n",
       "Data variables:\n",
       "    beta_ic     (chain, draw) float64 4kB 1.383 -0.07456 ... -0.921 1.762\n",
       "    beta_oc     (chain, draw) float64 4kB -0.2187 1.131 ... -1.086 -0.03639\n",
       "    p           (chain, draw, obs, alts_probs) float64 18MB 1.347e-100 ... 0.0\n",
       "Attributes:\n",
       "    created_at:                 2024-06-23T20:32:10.756506+00:00\n",
       "    arviz_version:              0.18.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.15.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-028392a0-9ef0-4342-bb3c-c002a78fe3ac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-028392a0-9ef0-4342-bb3c-c002a78fe3ac' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>obs</span>: 900</li><li><span class='xr-has-index'>alts_probs</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-feb30cd0-c2a0-4462-8dc6-d45992cea4d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-feb30cd0-c2a0-4462-8dc6-d45992cea4d6' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-1e8f21c6-b543-475e-be7e-08891c7b8967' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e8f21c6-b543-475e-be7e-08891c7b8967' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-798a7fc4-d5bf-4d1d-b471-6f881cef590d' class='xr-var-data-in' type='checkbox'><label for='data-798a7fc4-d5bf-4d1d-b471-6f881cef590d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-3467d090-f246-4881-a325-096e694f6a45' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3467d090-f246-4881-a325-096e694f6a45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42588a69-d08e-4247-a8b5-e0ea76908179' class='xr-var-data-in' type='checkbox'><label for='data-42588a69-d08e-4247-a8b5-e0ea76908179' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-b83aee6b-04fa-4aba-9f18-500ea72b0087' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b83aee6b-04fa-4aba-9f18-500ea72b0087' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83d0d074-198c-4c58-8017-880ed039efdd' class='xr-var-data-in' type='checkbox'><label for='data-83d0d074-198c-4c58-8017-880ed039efdd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alts_probs</span></div><div class='xr-var-dims'>(alts_probs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</div><input id='attrs-b2bf25cd-8a47-4b69-b618-9c83c4093d43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2bf25cd-8a47-4b69-b618-9c83c4093d43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-019cb3c8-13db-4623-bcf5-1df8db53bca1' class='xr-var-data-in' type='checkbox'><label for='data-019cb3c8-13db-4623-bcf5-1df8db53bca1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35839596-e0bd-44fe-9ec8-4321cfdf9aa9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35839596-e0bd-44fe-9ec8-4321cfdf9aa9' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta_ic</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.383 -0.07456 ... -0.921 1.762</div><input id='attrs-bf236f93-4932-46fc-95f6-eb7418a048f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf236f93-4932-46fc-95f6-eb7418a048f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9133cba1-4fac-418e-a0f3-a0e3eb8e8e96' class='xr-var-data-in' type='checkbox'><label for='data-9133cba1-4fac-418e-a0f3-a0e3eb8e8e96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1.38326650e+00, -7.45587282e-02, -5.81792481e-01,\n",
       "         1.64583486e+00,  9.81388918e-01,  3.20754793e-01,\n",
       "        -1.44680278e+00, -2.06025789e-01,  8.64762917e-01,\n",
       "         1.73868916e+00,  3.60089734e-01,  3.88279399e-01,\n",
       "         4.46724859e-01, -1.92723685e-01,  3.77030938e-02,\n",
       "         2.26743914e+00, -1.66789474e-01,  1.26919707e+00,\n",
       "        -5.12032636e-01, -5.55252577e-01, -4.96782131e-02,\n",
       "        -2.52394339e-01,  1.42892434e+00,  3.51714532e-01,\n",
       "        -2.22922874e-01, -3.50169472e-01,  1.97139329e+00,\n",
       "         7.95475057e-01,  6.25534373e-01,  4.41159488e-01,\n",
       "        -8.67342839e-01,  5.07451471e-01, -1.94339389e-01,\n",
       "         7.84437654e-01,  9.00848272e-01, -1.58365931e+00,\n",
       "        -3.94153497e-01,  4.73934137e-01, -2.32096110e-01,\n",
       "         1.52679971e+00, -7.33090872e-01,  5.40440435e-02,\n",
       "         4.20206750e-01,  1.68133009e-01,  8.98569623e-01,\n",
       "        -1.20163327e+00, -9.75584243e-01, -1.18505918e+00,\n",
       "         3.22548915e-01, -6.53172821e-01, -2.50655596e+00,\n",
       "         6.38678217e-01, -1.14182974e+00,  1.16539989e+00,\n",
       "         1.63830149e+00,  6.32884908e-01, -2.42133428e-01,\n",
       "         2.01297425e-01, -4.94791176e-02,  3.85060974e-02,\n",
       "...\n",
       "        -1.56106162e+00, -2.84616072e-01,  1.08531916e+00,\n",
       "         4.81523002e-01,  7.11481104e-01, -5.44618716e-01,\n",
       "        -1.25751968e+00, -6.89922007e-01, -1.68533020e-01,\n",
       "         1.14727326e-01,  8.37509204e-01,  3.64710178e-01,\n",
       "         5.81156618e-02, -5.94463594e-02, -4.25593140e-03,\n",
       "        -5.75202823e-01, -1.94502686e+00, -7.01523024e-01,\n",
       "         3.64551865e-01, -4.26902836e-01, -8.22513091e-02,\n",
       "         1.26542226e+00, -2.29849186e+00,  2.77434019e-01,\n",
       "         5.18548735e-01, -8.32959448e-01,  9.90450125e-01,\n",
       "         1.65323866e+00, -4.51926171e-01,  1.24054403e+00,\n",
       "         1.01275885e+00, -1.42925187e+00, -1.12880836e-01,\n",
       "        -3.48517306e-01, -8.73964461e-01, -4.05496974e-01,\n",
       "         1.25652380e+00, -2.11422792e-02,  9.51226150e-01,\n",
       "         1.19009122e+00, -1.09275551e-01,  8.38360828e-01,\n",
       "         1.77667865e-01, -1.11184601e+00, -2.60523819e-01,\n",
       "         9.41971128e-01,  1.34351006e+00,  2.35053486e-03,\n",
       "        -1.24979756e+00, -1.46555611e+00, -9.42550642e-01,\n",
       "         3.34928240e-01, -1.76595489e+00,  1.86342911e-01,\n",
       "         2.20608550e-01, -2.34437706e-01,  1.11062114e+00,\n",
       "        -9.21044778e-01,  1.76173066e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_oc</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.2187 1.131 ... -1.086 -0.03639</div><input id='attrs-8f3fc242-dfdf-4d11-9c60-2d74b65c1ee6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f3fc242-dfdf-4d11-9c60-2d74b65c1ee6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b880d77d-b229-4559-9af5-4791a941b62e' class='xr-var-data-in' type='checkbox'><label for='data-b880d77d-b229-4559-9af5-4791a941b62e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-2.18724272e-01,  1.13098028e+00, -9.37282697e-01,\n",
       "         2.64177807e-01, -1.17734793e+00, -1.37330362e+00,\n",
       "        -1.73970760e-01, -1.36896541e+00,  3.85066116e-02,\n",
       "         8.64166726e-01,  1.68653751e+00, -5.45321315e-01,\n",
       "         1.01895759e+00,  8.90660529e-02,  4.44408281e-01,\n",
       "         1.96294788e-01,  6.89205806e-01,  1.14307422e+00,\n",
       "         3.35997470e-01, -1.61744354e+00,  9.89014201e-01,\n",
       "        -2.36364200e+00, -7.47573088e-01,  1.07466100e+00,\n",
       "        -2.17134798e+00,  1.29394173e-01, -5.31345888e-01,\n",
       "         1.02857117e-01,  1.41281542e+00,  2.17402322e+00,\n",
       "         7.17895189e-01, -7.74930820e-01,  1.24237597e+00,\n",
       "        -4.65215265e-01,  1.37410741e+00,  8.68770005e-01,\n",
       "        -2.38616653e-01,  5.33523384e-01, -8.05903979e-01,\n",
       "        -6.98977110e-01, -1.44637213e+00, -1.09441735e-01,\n",
       "        -5.03454300e-01,  6.20235572e-01,  4.13557688e-01,\n",
       "         1.06777670e+00, -5.84416370e-01,  2.17311742e+00,\n",
       "        -1.42837875e+00,  5.28116978e-01, -4.96708329e-01,\n",
       "        -6.29428281e-01, -6.06128256e-01,  7.84153504e-01,\n",
       "         1.53397302e-01, -1.02352567e+00, -3.43940374e-01,\n",
       "        -3.70735143e-01, -2.21844482e-01,  1.84997223e+00,\n",
       "...\n",
       "        -1.21051799e+00, -3.67753776e-01,  1.00004975e+00,\n",
       "        -1.30402450e-01,  1.08847436e-01, -8.06239420e-01,\n",
       "         3.04261341e-01, -2.75164749e-01, -5.39020456e-01,\n",
       "        -1.07610620e-01,  2.18530560e+00,  4.41874710e-01,\n",
       "        -2.37728559e-01, -4.50805654e-01,  2.72418581e-01,\n",
       "        -7.38092586e-01, -2.34393681e+00,  5.21358491e-01,\n",
       "        -1.02342427e+00, -2.27437571e-01,  8.34447225e-01,\n",
       "         5.25093339e-01, -9.74835311e-01,  1.28344006e+00,\n",
       "         1.31464981e+00, -1.21745550e-01, -2.18126345e-01,\n",
       "         2.30982110e-01, -1.40217342e+00,  6.51477656e-01,\n",
       "         8.27257637e-01, -1.74879594e-01,  1.34584346e+00,\n",
       "         1.35776443e+00, -1.46789158e+00,  9.23491710e-02,\n",
       "        -1.54262898e-01,  9.00426851e-01, -9.76272546e-01,\n",
       "         5.84051037e-01, -7.20015378e-02,  3.90502365e-01,\n",
       "        -1.00072682e+00,  6.26765863e-01, -6.57189342e-01,\n",
       "         5.71041199e-01, -4.38164033e-01, -1.44436373e+00,\n",
       "         4.67760783e-01,  1.38629530e+00,  3.73237264e-01,\n",
       "        -9.37224147e-01,  1.95161086e-01, -9.65195629e-01,\n",
       "        -1.61252987e+00,  3.06965268e-01, -1.25784537e-01,\n",
       "        -1.08614370e+00, -3.63936865e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(chain, draw, obs, alts_probs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.347e-100 1.911e-14 ... 1.0 0.0</div><input id='attrs-23d46628-b313-41d8-abe5-64a313426fa7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23d46628-b313-41d8-abe5-64a313426fa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d1b0360-1468-441f-81f8-0930419f4490' class='xr-var-data-in' type='checkbox'><label for='data-1d1b0360-1468-441f-81f8-0930419f4490' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[1.34671297e-100, 1.91139328e-014, 2.43947490e-063,\n",
       "          1.00000000e+000, 0.00000000e+000],\n",
       "         [9.98040992e-063, 1.00000000e+000, 1.02487442e-070,\n",
       "          4.07132826e-052, 0.00000000e+000],\n",
       "         [2.85515600e-112, 1.00000000e+000, 1.30621444e-158,\n",
       "          1.08048464e-045, 0.00000000e+000],\n",
       "         ...,\n",
       "         [2.30156887e-160, 3.08592709e-038, 5.83005298e-165,\n",
       "          1.00000000e+000, 0.00000000e+000],\n",
       "         [6.06929797e-204, 9.61695016e-001, 1.99988451e-140,\n",
       "          3.83049845e-002, 0.00000000e+000],\n",
       "         [3.26382940e-124, 4.08947632e-044, 4.62453374e-136,\n",
       "          1.00000000e+000, 0.00000000e+000]],\n",
       "\n",
       "        [[1.00000000e+000, 1.41883359e-028, 1.25084719e-174,\n",
       "          2.54756743e-201, 1.13562547e-244],\n",
       "         [1.00000000e+000, 1.79329481e-020, 3.48460260e-171,\n",
       "          3.46461278e-172, 1.86502610e-230],\n",
       "         [1.00000000e+000, 2.02385296e-023, 3.71931404e-131,\n",
       "          9.58177773e-151, 4.49320481e-193],\n",
       "...\n",
       "         [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "          0.00000000e+000, 1.00000000e+000],\n",
       "         [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "          0.00000000e+000, 1.00000000e+000],\n",
       "         [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "          0.00000000e+000, 1.00000000e+000]],\n",
       "\n",
       "        [[1.98431291e-105, 1.00000000e+000, 3.58445356e-095,\n",
       "          1.78993656e-020, 0.00000000e+000],\n",
       "         [3.84818059e-076, 1.00000000e+000, 2.71493217e-123,\n",
       "          1.32300540e-099, 0.00000000e+000],\n",
       "         [3.51959229e-139, 1.00000000e+000, 5.82198856e-227,\n",
       "          4.52246690e-086, 0.00000000e+000],\n",
       "         ...,\n",
       "         [1.42197003e-168, 3.25818873e-016, 2.21723790e-206,\n",
       "          1.00000000e+000, 0.00000000e+000],\n",
       "         [1.12932342e-249, 1.00000000e+000, 1.15561543e-198,\n",
       "          7.89297474e-029, 0.00000000e+000],\n",
       "         [2.17119127e-122, 1.05514439e-026, 1.52717246e-173,\n",
       "          1.00000000e+000, 0.00000000e+000]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e8ea89a6-4643-497b-9a8c-dce4ca3945ac' class='xr-section-summary-in' type='checkbox'  ><label for='section-e8ea89a6-4643-497b-9a8c-dce4ca3945ac' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dc15d132-3205-475d-8c76-1e22a17abdac' class='xr-index-data-in' type='checkbox'/><label for='index-dc15d132-3205-475d-8c76-1e22a17abdac' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-67b37812-7706-4b6f-9a0b-3963f8797d0a' class='xr-index-data-in' type='checkbox'/><label for='index-67b37812-7706-4b6f-9a0b-3963f8797d0a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-05e5bc09-13c0-4472-bbd6-f9d254a4ff01' class='xr-index-data-in' type='checkbox'/><label for='index-05e5bc09-13c0-4472-bbd6-f9d254a4ff01' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>alts_probs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cd8b0740-faf4-43e6-846a-9e194ad38b4d' class='xr-index-data-in' type='checkbox'/><label for='index-cd8b0740-faf4-43e6-846a-9e194ad38b4d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;alts_probs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c08b3934-25fc-42d4-9d16-3e8f81c02a57' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c08b3934-25fc-42d4-9d16-3e8f81c02a57' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-23T20:32:10.756506+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.15.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_prior_predictivec31f33e2-b45b-4129-92c4-3c87cf0753c1\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_prior_predictivec31f33e2-b45b-4129-92c4-3c87cf0753c1\" class = \"xr-section-summary\">prior_predictive</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
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       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4MB\n",
       "Dimensions:  (chain: 1, draw: 500, obs: 900)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 8B 0\n",
       "  * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
       "  * obs      (obs) int64 7kB 0 1 2 3 4 5 6 7 ... 892 893 894 895 896 897 898 899\n",
       "Data variables:\n",
       "    y_cat    (chain, draw, obs) int64 4MB 3 1 1 2 1 3 3 3 3 ... 1 1 1 3 1 3 1 3\n",
       "Attributes:\n",
       "    created_at:                 2024-06-23T20:32:10.758790+00:00\n",
       "    arviz_version:              0.18.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.15.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dddde8cb-532f-4205-a3a4-92d82b735ead' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dddde8cb-532f-4205-a3a4-92d82b735ead' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>obs</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fbde4598-5499-48d8-b3b5-2728e927de50' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fbde4598-5499-48d8-b3b5-2728e927de50' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-936ba333-0af9-4991-829f-3ea686dc03c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-936ba333-0af9-4991-829f-3ea686dc03c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abe1eb47-4dde-4138-bd4a-22bfadb83754' class='xr-var-data-in' type='checkbox'><label for='data-abe1eb47-4dde-4138-bd4a-22bfadb83754' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-e048e226-8c07-4781-9d72-a464cf09ed46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e048e226-8c07-4781-9d72-a464cf09ed46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a9baa64-31d8-4d26-b47a-50474eb341f4' class='xr-var-data-in' type='checkbox'><label for='data-8a9baa64-31d8-4d26-b47a-50474eb341f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-0a34033f-ca32-4d0d-ba83-7513d312d3b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a34033f-ca32-4d0d-ba83-7513d312d3b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd35074b-d46c-4245-a6a3-aa57fdfb634d' class='xr-var-data-in' type='checkbox'><label for='data-dd35074b-d46c-4245-a6a3-aa57fdfb634d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-851528a2-1947-4674-aabf-b549399bb005' class='xr-section-summary-in' type='checkbox'  checked><label for='section-851528a2-1947-4674-aabf-b549399bb005' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(chain, draw, obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 1 1 2 1 3 3 3 ... 1 1 1 3 1 3 1 3</div><input id='attrs-4441ad12-0834-449a-9112-939e0dc32be1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4441ad12-0834-449a-9112-939e0dc32be1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-265f81a0-c853-45eb-89d9-e2c11eece11f' class='xr-var-data-in' type='checkbox'><label for='data-265f81a0-c853-45eb-89d9-e2c11eece11f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[3, 1, 1, ..., 3, 3, 3],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [4, 4, 4, ..., 4, 4, 4],\n",
       "        ...,\n",
       "        [3, 1, 1, ..., 3, 1, 3],\n",
       "        [4, 4, 4, ..., 4, 4, 4],\n",
       "        [1, 1, 1, ..., 3, 1, 3]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-686c478b-736a-4cdc-8c59-265884946f9b' class='xr-section-summary-in' type='checkbox'  ><label for='section-686c478b-736a-4cdc-8c59-265884946f9b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-50e43a1d-144d-4d59-96f4-32f77fb83e10' class='xr-index-data-in' type='checkbox'/><label for='index-50e43a1d-144d-4d59-96f4-32f77fb83e10' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8f75f26-0b4f-44b3-a5cd-fbc9de8b8a15' class='xr-index-data-in' type='checkbox'/><label for='index-b8f75f26-0b4f-44b3-a5cd-fbc9de8b8a15' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d4d6a77c-9920-4b57-a576-0da2ff4cf3cf' class='xr-index-data-in' type='checkbox'/><label for='index-d4d6a77c-9920-4b57-a576-0da2ff4cf3cf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55df647c-3249-43ab-9cd9-21609e68623a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-55df647c-3249-43ab-9cd9-21609e68623a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-23T20:32:10.758790+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.15.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_dataff3e5248-ed40-4de5-85dc-c51656b65891\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_dataff3e5248-ed40-4de5-85dc-c51656b65891\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
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       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 14kB\n",
       "Dimensions:  (obs: 900)\n",
       "Coordinates:\n",
       "  * obs      (obs) int64 7kB 0 1 2 3 4 5 6 7 ... 892 893 894 895 896 897 898 899\n",
       "Data variables:\n",
       "    y_cat    (obs) int64 7kB 2 2 2 1 1 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 1 2 2 2 2 2\n",
       "Attributes:\n",
       "    created_at:                 2024-06-23T20:32:10.759288+00:00\n",
       "    arviz_version:              0.18.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.15.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6173df40-5b3b-414a-bed5-f0bbdf7e1a76' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6173df40-5b3b-414a-bed5-f0bbdf7e1a76' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>obs</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-be9db276-f21b-49a9-8038-46c0749b67c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-be9db276-f21b-49a9-8038-46c0749b67c5' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-4ccb3487-4c51-4b5d-a726-9f7c3ba8410d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ccb3487-4c51-4b5d-a726-9f7c3ba8410d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fdcb92e-8ade-461a-b018-32603461b346' class='xr-var-data-in' type='checkbox'><label for='data-6fdcb92e-8ade-461a-b018-32603461b346' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d84cca6-1668-4b68-9045-ab4d85f4df9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d84cca6-1668-4b68-9045-ab4d85f4df9b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 2 2 1 1 2 2 2 ... 2 2 1 2 2 2 2 2</div><input id='attrs-39ae0f54-d9b3-4df8-9001-c13f46c079a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39ae0f54-d9b3-4df8-9001-c13f46c079a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-874fc936-04e2-4e55-baa8-643596d244a1' class='xr-var-data-in' type='checkbox'><label for='data-874fc936-04e2-4e55-baa8-643596d244a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 4, 4, 1, 2, 0, 3,\n",
       "       2, 4, 2, 3, 2, 0, 0, 2, 2, 3, 3, 2, 2, 2, 3, 2, 2, 1, 2, 3, 2, 2,\n",
       "       2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 4, 3, 2, 4, 2, 2, 2, 2, 2, 2, 3, 2,\n",
       "       2, 2, 3, 2, 3, 2, 1, 4, 1, 2, 2, 2, 1, 3, 2, 2, 2, 4, 3, 2, 2, 2,\n",
       "       2, 2, 4, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 2, 2, 2, 4, 2,\n",
       "       2, 2, 4, 1, 0, 2, 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3,\n",
       "       0, 3, 2, 3, 1, 0, 2, 2, 3, 2, 1, 4, 4, 3, 2, 3, 2, 2, 4, 3, 4, 2,\n",
       "       2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 2, 2, 0, 1, 2, 2, 3, 2, 2,\n",
       "       2, 2, 3, 3, 1, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 0, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 0, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 3, 2, 0, 2, 2, 2, 2,\n",
       "       2, 3, 2, 2, 3, 2, 1, 0, 0, 4, 2, 2, 2, 2, 2, 1, 2, 3, 3, 3, 2, 2,\n",
       "       2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 4, 0, 2, 1,\n",
       "       3, 1, 2, 3, 2, 3, 0, 0, 4, 2, 2, 2, 2, 2, 0, 1, 2, 2, 2, 2, 2, 2,\n",
       "       2, 1, 1, 2, 1, 3, 1, 2, 4, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 3, 2, 2,\n",
       "       2, 2, 1, 3, 2, 3, 2, 2, 2, 4, 4, 2, 3, 3, 3, 1, 2, 2, 2, 2, 2, 2,\n",
       "       4, 2, 2, 2, 4, 2, 0, 2, 3, 4, 2, 2, 2, 2, 2, 2, 2, 4, 0, 2, 1, 3,\n",
       "       0, 1, 2, 2, 2, 2, 2, 1, 2, 4, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2,\n",
       "       2, 0, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 0, 3, 0, 2, 2, 3, 3, 2, 1, 0,\n",
       "       2, 1, 2, 2, 2, 2, 3, 0, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "...\n",
       "       3, 2, 1, 2, 3, 2, 3, 3, 3, 2, 0, 2, 2, 3, 1, 2, 0, 1, 3, 2, 1, 0,\n",
       "       2, 3, 0, 0, 1, 3, 1, 2, 2, 2, 2, 4, 2, 1, 2, 2, 2, 2, 1, 2, 2, 3,\n",
       "       3, 2, 2, 2, 1, 2, 4, 2, 3, 0, 2, 1, 2, 1, 2, 2, 3, 2, 2, 2, 2, 3,\n",
       "       2, 2, 2, 2, 2, 1, 3, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 1, 2, 0, 2, 2,\n",
       "       2, 3, 2, 3, 2, 0, 2, 3, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 4, 3, 2,\n",
       "       3, 2, 2, 2, 2, 0, 2, 2, 3, 0, 2, 1, 1, 3, 2, 2, 2, 2, 3, 0, 3, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 4, 0, 4, 2, 1, 2, 1, 2, 2, 0, 2, 2, 1, 2,\n",
       "       3, 2, 3, 3, 2, 2, 4, 1, 2, 2, 2, 2, 4, 4, 1, 2, 2, 2, 1, 2, 1, 3,\n",
       "       3, 0, 4, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 4, 2, 1, 1, 0,\n",
       "       2, 2, 2, 2, 1, 1, 3, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 3, 0, 2, 2, 2,\n",
       "       1, 2, 3, 4, 2, 1, 2, 3, 2, 3, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 4, 2, 2, 2, 2, 2, 1, 2, 0, 2, 2, 3, 0, 3, 2, 2, 2, 3, 3,\n",
       "       2, 3, 2, 2, 2, 2, 2, 1, 4, 3, 2, 2, 4, 0, 3, 2, 1, 1, 2, 2, 2, 2,\n",
       "       0, 2, 2, 0, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 2, 3, 2, 2, 2, 1, 4, 0,\n",
       "       2, 2, 2, 2, 2, 2, 2, 1, 4, 2, 2, 0, 2, 1, 2, 2, 0, 2, 3, 2, 2, 3,\n",
       "       3, 4, 2, 3, 2, 0, 2, 3, 2, 4, 3, 3, 2, 2, 2, 1, 2, 2, 1, 2, 2, 3,\n",
       "       0, 2, 2, 3, 2, 2, 1, 2, 3, 2, 2, 2, 2, 4, 1, 2, 2, 2, 2, 2, 3, 2,\n",
       "       1, 2, 1, 0, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 0, 2, 1, 2, 2, 2, 2, 2,\n",
       "       1, 1, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 1, 3, 2, 2, 4, 4, 2, 2, 2,\n",
       "       1, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3bbc59f3-7df2-40b1-a457-9d5e5e892051' class='xr-section-summary-in' type='checkbox'  ><label for='section-3bbc59f3-7df2-40b1-a457-9d5e5e892051' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-581686d6-8ef3-4a83-a730-9c36d79110cb' class='xr-index-data-in' type='checkbox'/><label for='index-581686d6-8ef3-4a83-a730-9c36d79110cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0709ed4-ac25-41b0-9ca0-2dfbb6ec2f22' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0709ed4-ac25-41b0-9ca0-2dfbb6ec2f22' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-23T20:32:10.759288+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.15.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-sections.group-sections {\n",
       "  grid-template-columns: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> posterior_predictive\n",
       "\t> log_likelihood\n",
       "\t> sample_stats\n",
       "\t> prior\n",
       "\t> prior_predictive\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata_m1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beta_ic</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>2209.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_oc</th>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>983.0</td>\n",
       "      <td>1241.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean   sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "beta_ic  0.002  0.0   0.002    0.002        0.0      0.0    1802.0    2209.0   \n",
       "beta_oc -0.004  0.0  -0.005   -0.004        0.0      0.0     983.0    1241.0   \n",
       "\n",
       "         r_hat  \n",
       "beta_ic    1.0  \n",
       "beta_oc    1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summaries = az.summary(idata_m1, var_names=[\"beta_ic\", \"beta_oc\"])\n",
    "summaries # results are sus because beta_ic and beta_oc should both be negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.211196691253724"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## marginal rate of substitution for a reduction in installation costs\n",
    "post = az.extract(idata_m1)\n",
    "substitution_rate = post[\"beta_oc\"] / post[\"beta_ic\"]\n",
    "substitution_rate.mean().item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1011,
       "width": 2011
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "ax.hist(\n",
    "    substitution_rate,\n",
    "    bins=30,\n",
    "    ec=\"black\",\n",
    ")\n",
    "ax.set_title(\"Uncertainty in Marginal Rate of Substitution \\n Operating Costs / Installation Costs\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;p&#x27; (alts_probs: 5)&gt; Size: 40B\n",
       "array([0.08432415, 0.13772203, 0.26912233, 0.38199249, 0.12683901])\n",
       "Coordinates:\n",
       "  * alts_probs  (alts_probs) &lt;U2 40B &#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'p'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>alts_probs</span>: 5</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4d39ca9b-73b7-46f7-9db5-851ae81b5b3e' class='xr-array-in' type='checkbox' checked><label for='section-4d39ca9b-73b7-46f7-9db5-851ae81b5b3e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.08432 0.1377 0.2691 0.382 0.1268</span></div><div class='xr-array-data'><pre>array([0.08432415, 0.13772203, 0.26912233, 0.38199249, 0.12683901])</pre></div></div></li><li class='xr-section-item'><input id='section-487cf58a-365e-4a9d-8fb3-0a750ae48be1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-487cf58a-365e-4a9d-8fb3-0a750ae48be1' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alts_probs</span></div><div class='xr-var-dims'>(alts_probs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</div><input id='attrs-6257bad0-b347-4d3f-b9b3-f26e894c4a26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6257bad0-b347-4d3f-b9b3-f26e894c4a26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a30bf30-d9d6-4aed-a622-b1f95c88e249' class='xr-var-data-in' type='checkbox'><label for='data-1a30bf30-d9d6-4aed-a622-b1f95c88e249' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75ad4825-16c7-42a8-9e37-8040118bb88b' class='xr-section-summary-in' type='checkbox'  ><label for='section-75ad4825-16c7-42a8-9e37-8040118bb88b' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>alts_probs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9736d760-980d-4ba4-bcb1-2a1ab9358698' class='xr-index-data-in' type='checkbox'/><label for='index-9736d760-980d-4ba4-bcb1-2a1ab9358698' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;alts_probs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a755e6f5-478e-4d07-a82f-97efc025ea3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a755e6f5-478e-4d07-a82f-97efc025ea3a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'p' (alts_probs: 5)> Size: 40B\n",
       "array([0.08432415, 0.13772203, 0.26912233, 0.38199249, 0.12683901])\n",
       "Coordinates:\n",
       "  * alts_probs  (alts_probs) <U2 40B 'ec' 'er' 'gc' 'gr' 'hp'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata_m1[\"posterior\"][\"p\"].mean(dim=[\"chain\", \"draw\", \"obs\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1011,
       "width": 2011
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(20, 10))\n",
    "ax = axs[0]\n",
    "counts = wide_heating_df.groupby(\"depvar\")[\"idcase\"].count()\n",
    "predicted_shares = idata_m1[\"posterior\"][\"p\"].mean(dim=[\"chain\", \"draw\", \"obs\"])\n",
    "ci_lb = idata_m1[\"posterior\"][\"p\"].quantile(0.025, dim=[\"chain\", \"draw\", \"obs\"])\n",
    "ci_ub = idata_m1[\"posterior\"][\"p\"].quantile(0.975, dim=[\"chain\", \"draw\", \"obs\"])\n",
    "ax.scatter(ci_lb, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
    "ax.scatter(ci_ub, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
    "ax.scatter(\n",
    "    counts / counts.sum(),\n",
    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
    "    label=\"Observed Shares\",\n",
    "    color=\"red\",\n",
    "    s=100,\n",
    ")\n",
    "ax.hlines(\n",
    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], ci_lb, ci_ub, label=\"Predicted 95% Interval\", color=\"black\"\n",
    ")\n",
    "ax.legend()\n",
    "ax.set_title(\"Observed V Predicted Shares\")\n",
    "az.plot_ppc(idata_m1, ax=axs[1])\n",
    "axs[1].set_title(\"Posterior Predictive Checks\")\n",
    "ax.set_xlabel(\"Shares\")\n",
    "ax.set_ylabel(\"Heating System\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting a better fit\n",
    "\n",
    "You can improve the fit by adding a good-specific intercept -- intuitively this should allow you to match the observed frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alpha, beta_ic, beta_oc, y_cat]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b82f4a46a8f421a8f51e663c1c789c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b79a6eb2e6154ca2ad2354ba8dc87162",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15cf230c6baa418d9d33383230068dc1",
       "version_major": 2,
       "version_minor": 0
      },
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       "  0%|          | 0/2000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  0%|          | 0/2000 [00:00<?, ?it/s]"
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      "Sampling: [y_cat]\n"
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       "Output()"
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     "metadata": {},
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   ],
   "source": [
    "N = wide_heating_df.shape[0]\n",
    "observed = pd.Categorical(wide_heating_df[\"depvar\"]).codes\n",
    "\n",
    "coords = {\n",
    "    \"alts_intercepts\": [\"ec\", \"er\", \"gc\", \"gr\"],\n",
    "    \"alts_probs\": [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
    "    \"obs\": range(N),\n",
    "}\n",
    "with pm.Model(coords=coords) as model_2:\n",
    "    beta_ic = pm.Normal(\"beta_ic\", 0, 1)\n",
    "    beta_oc = pm.Normal(\"beta_oc\", 0, 1)\n",
    "    alphas = pm.Normal(\"alpha\", 0, 1, dims=\"alts_intercepts\")\n",
    "\n",
    "    ## Construct Utility matrix and Pivot using an intercept per alternative\n",
    "    u0 = alphas[0] + beta_ic * wide_heating_df[\"ic.ec\"] + beta_oc * wide_heating_df[\"oc.ec\"]\n",
    "    u1 = alphas[1] + beta_ic * wide_heating_df[\"ic.er\"] + beta_oc * wide_heating_df[\"oc.er\"]\n",
    "    u2 = alphas[2] + beta_ic * wide_heating_df[\"ic.gc\"] + beta_oc * wide_heating_df[\"oc.gc\"]\n",
    "    u3 = alphas[3] + beta_ic * wide_heating_df[\"ic.gr\"] + beta_oc * wide_heating_df[\"oc.gr\"]\n",
    "    u4 = np.zeros(N)  # Outside Good\n",
    "    s = pm.math.stack([u0, u1, u2, u3, u4]).T\n",
    "\n",
    "    ## Apply Softmax Transform\n",
    "    p_ = pm.Deterministic(\"p\", pm.math.softmax(s, axis=1), dims=(\"obs\", \"alts_probs\"))\n",
    "\n",
    "    ## Likelihood\n",
    "    choice_obs = pm.Categorical(\"y_cat\", p=p_, observed=observed, dims=\"obs\")\n",
    "\n",
    "    idata_m2 = pm.sample_prior_predictive()\n",
    "    idata_m2.extend(\n",
    "        pm.sample(nuts_sampler=\"numpyro\", idata_kwargs={\"log_likelihood\": True}, random_seed=103)\n",
    "    )\n",
    "    idata_m2.extend(pm.sample_posterior_predictive(idata_m2))\n",
    "\n",
    "\n",
    "pm.model_to_graphviz(model_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beta_ic</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>1904.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_oc</th>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[ec]</th>\n",
       "      <td>1.063</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.087</td>\n",
       "      <td>2.054</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.012</td>\n",
       "      <td>914.0</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[er]</th>\n",
       "      <td>1.102</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.135</td>\n",
       "      <td>2.017</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.012</td>\n",
       "      <td>856.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[gc]</th>\n",
       "      <td>2.397</td>\n",
       "      <td>0.320</td>\n",
       "      <td>1.826</td>\n",
       "      <td>3.019</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.008</td>\n",
       "      <td>874.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[gr]</th>\n",
       "      <td>0.760</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1.448</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.009</td>\n",
       "      <td>957.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "beta_ic    0.001  0.000  -0.000    0.002      0.000    0.000    1324.0   \n",
       "beta_oc   -0.003  0.001  -0.005   -0.001      0.000    0.000    1337.0   \n",
       "alpha[ec]  1.063  0.513   0.087    2.054      0.017    0.012     914.0   \n",
       "alpha[er]  1.102  0.491   0.135    2.017      0.017    0.012     856.0   \n",
       "alpha[gc]  2.397  0.320   1.826    3.019      0.011    0.008     874.0   \n",
       "alpha[gr]  0.760  0.385   0.036    1.448      0.012    0.009     957.0   \n",
       "\n",
       "           ess_tail  r_hat  \n",
       "beta_ic      1904.0   1.00  \n",
       "beta_oc      1785.0   1.00  \n",
       "alpha[ec]    1150.0   1.00  \n",
       "alpha[er]     900.0   1.01  \n",
       "alpha[gc]    1002.0   1.01  \n",
       "alpha[gr]    1124.0   1.01  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_m2, var_names=[\"beta_ic\", \"beta_oc\", \"alpha\"])\n",
    "\n",
    "# model is still terrible, installation costs and operational costs are meaningless, it's all about the intercepts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1011,
       "width": 2011
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(20, 10))\n",
    "ax = axs[0]\n",
    "counts = wide_heating_df.groupby(\"depvar\")[\"idcase\"].count()\n",
    "predicted_shares = idata_m2[\"posterior\"][\"p\"].mean(dim=[\"chain\", \"draw\", \"obs\"])\n",
    "ci_lb = idata_m2[\"posterior\"][\"p\"].quantile(0.025, dim=[\"chain\", \"draw\", \"obs\"])\n",
    "ci_ub = idata_m2[\"posterior\"][\"p\"].quantile(0.975, dim=[\"chain\", \"draw\", \"obs\"])\n",
    "\n",
    "ax.scatter(ci_lb, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
    "ax.scatter(ci_ub, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
    "ax.scatter(\n",
    "    counts / counts.sum(),\n",
    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
    "    label=\"Observed Shares\",\n",
    "    color=\"red\",\n",
    "    s=100,\n",
    ")\n",
    "ax.hlines(\n",
    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], ci_lb, ci_ub, label=\"Predicted 95% Interval\", color=\"black\"\n",
    ")\n",
    "ax.legend()\n",
    "ax.set_title(\"Observed V Predicted Shares\")\n",
    "az.plot_ppc(idata_m2, ax=axs[1])\n",
    "axs[1].set_title(\"Posterior Predictive Checks\")\n",
    "ax.set_xlabel(\"Shares\")\n",
    "ax.set_ylabel(\"Heating System\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-11.215830921463027"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## marginal rate of substitution for a reduction in installation costs\n",
    "post = az.extract(idata_m2)\n",
    "substitution_rate = post[\"beta_oc\"] / post[\"beta_ic\"]\n",
    "substitution_rate.mean().item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1011,
       "width": 2011
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "ax.hist(\n",
    "    substitution_rate,\n",
    "    bins=30,\n",
    "    ec=\"black\",\n",
    ")\n",
    "ax.set_title(\"Uncertainty in Marginal Rate of Substitution \\n Operating Costs / Installation Costs\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bells and Whistles\n",
    "\n",
    "Adding some cool extra features by:\n",
    "- using a multivariate normal distirbution -- this allows for correlation among alternatives\n",
    "- controlling for income in a log-affine fashion\n",
    "\n",
    "Intuitively not sure how the correlation is identified, given that you only observe the actual choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alpha, beta_ic, beta_income, beta_oc, chol, y_cat]\n"
     ]
    },
    {
     "data": {
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       "model_id": "a04baefb8f2249d08d305d88772d62c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84c1490d0cb544c58ade3352d5b0f7ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7c07b909889477895c5378c498e0cd7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "617300181f484b91bb46d0b4da7eb346",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "There were 713 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
      "Sampling: [y_cat]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7171676a88e49018085545a1cc2ab60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n"
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    {
     "data": {
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       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-507.45 1001,-507.45 1001,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>cluster900</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M20,-255.73C20,-255.73 282,-255.73 282,-255.73 288,-255.73 294,-261.73 294,-267.73 294,-267.73 294,-341.73 294,-341.73 294,-347.73 288,-353.73 282,-353.73 282,-353.73 20,-353.73 20,-353.73 14,-353.73 8,-347.73 8,-341.73 8,-341.73 8,-267.73 8,-267.73 8,-261.73 14,-255.73 20,-255.73\"/>\n",
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       "<title>clusteralts_intercepts (4)</title>\n",
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       "<title>cluster10</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M742,-373.63C742,-373.63 902,-373.63 902,-373.63 908,-373.63 914,-379.63 914,-385.63 914,-385.63 914,-483.45 914,-483.45 914,-489.45 908,-495.45 902,-495.45 902,-495.45 742,-495.45 742,-495.45 736,-495.45 730,-489.45 730,-483.45 730,-483.45 730,-385.63 730,-385.63 730,-379.63 736,-373.63 742,-373.63\"/>\n",
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       "</g>\n",
       "<g id=\"clust4\" class=\"cluster\">\n",
       "<title>cluster4 x 4</title>\n",
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       "<text text-anchor=\"middle\" x=\"853.12\" y=\"-262.93\" font-family=\"Times,serif\" font-size=\"14.00\">4 x 4</text>\n",
       "</g>\n",
       "<g id=\"clust5\" class=\"cluster\">\n",
       "<title>cluster4</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M895,-255.73C895,-255.73 977,-255.73 977,-255.73 983,-255.73 989,-261.73 989,-267.73 989,-267.73 989,-341.73 989,-341.73 989,-347.73 983,-353.73 977,-353.73 977,-353.73 895,-353.73 895,-353.73 889,-353.73 883,-347.73 883,-341.73 883,-341.73 883,-267.73 883,-267.73 883,-261.73 889,-255.73 895,-255.73\"/>\n",
       "<text text-anchor=\"middle\" x=\"977.62\" y=\"-262.93\" font-family=\"Times,serif\" font-size=\"14.00\">4</text>\n",
       "</g>\n",
       "<g id=\"clust6\" class=\"cluster\">\n",
       "<title>clusterobs (900) x alts_probs (5)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M236,-137.82C236,-137.82 369,-137.82 369,-137.82 375,-137.82 381,-143.82 381,-149.82 381,-149.82 381,-223.82 381,-223.82 381,-229.82 375,-235.82 369,-235.82 369,-235.82 236,-235.82 236,-235.82 230,-235.82 224,-229.82 224,-223.82 224,-223.82 224,-149.82 224,-149.82 224,-143.82 230,-137.82 236,-137.82\"/>\n",
       "<text text-anchor=\"middle\" x=\"302.5\" y=\"-145.02\" font-family=\"Times,serif\" font-size=\"14.00\">obs (900) x alts_probs (5)</text>\n",
       "</g>\n",
       "<g id=\"clust7\" class=\"cluster\">\n",
       "<title>clusterobs (900)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M251,-8C251,-8 353,-8 353,-8 359,-8 365,-14 365,-20 365,-20 365,-117.82 365,-117.82 365,-123.82 359,-129.82 353,-129.82 353,-129.82 251,-129.82 251,-129.82 245,-129.82 239,-123.82 239,-117.82 239,-117.82 239,-20 239,-20 239,-14 245,-8 251,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"331.12\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">obs (900)</text>\n",
       "</g>\n",
       "<!-- ic_er -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>ic_er</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M58,-345.73C58,-345.73 28,-345.73 28,-345.73 22,-345.73 16,-339.73 16,-333.73 16,-333.73 16,-300.23 16,-300.23 16,-294.23 22,-288.23 28,-288.23 28,-288.23 58,-288.23 58,-288.23 64,-288.23 70,-294.23 70,-300.23 70,-300.23 70,-333.73 70,-333.73 70,-339.73 64,-345.73 58,-345.73\"/>\n",
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       "<text text-anchor=\"middle\" x=\"43\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"43\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
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       "<!-- p -->\n",
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       "<title>p</title>\n",
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       "<text text-anchor=\"middle\" x=\"302\" y=\"-210.52\" font-family=\"Times,serif\" font-size=\"14.00\">p</text>\n",
       "<text text-anchor=\"middle\" x=\"302\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"302\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
       "</g>\n",
       "<!-- ic_er&#45;&gt;p -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>ic_er&#45;&gt;p</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M50.31,-288.04C55.74,-272.71 64.74,-254.67 79,-243.82 104.55,-224.36 187.77,-211.96 244.99,-205.51\"/>\n",
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       "</g>\n",
       "<!-- oc_er -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>oc_er</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M130,-345.73C130,-345.73 100,-345.73 100,-345.73 94,-345.73 88,-339.73 88,-333.73 88,-333.73 88,-300.23 88,-300.23 88,-294.23 94,-288.23 100,-288.23 100,-288.23 130,-288.23 130,-288.23 136,-288.23 142,-294.23 142,-300.23 142,-300.23 142,-333.73 142,-333.73 142,-339.73 136,-345.73 130,-345.73\"/>\n",
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       "<!-- oc_er&#45;&gt;p -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>oc_er&#45;&gt;p</title>\n",
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       "<!-- ic_ec -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>ic_ec</title>\n",
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       "</g>\n",
       "<!-- ic_ec&#45;&gt;p -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>ic_ec&#45;&gt;p</title>\n",
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       "</g>\n",
       "<!-- oc_ec -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>oc_ec</title>\n",
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       "<text text-anchor=\"middle\" x=\"259\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">oc_ec</text>\n",
       "<text text-anchor=\"middle\" x=\"259\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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       "</g>\n",
       "<!-- oc_ec&#45;&gt;p -->\n",
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       "<title>oc_ec&#45;&gt;p</title>\n",
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       "<title>beta_oc</title>\n",
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       "<text text-anchor=\"middle\" x=\"345\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_oc&#45;&gt;p -->\n",
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       "<title>beta_oc&#45;&gt;p</title>\n",
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       "</g>\n",
       "<!-- beta_ic&#45;&gt;p -->\n",
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       "<title>beta_ic&#45;&gt;p</title>\n",
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       "<!-- beta_income&#45;&gt;p -->\n",
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       "<title>beta_income&#45;&gt;p</title>\n",
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       "<text text-anchor=\"middle\" x=\"698\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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       "<!-- alpha&#45;&gt;p -->\n",
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       "<title>alpha&#45;&gt;p</title>\n",
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       "<title>chol</title>\n",
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       "<text text-anchor=\"middle\" x=\"822\" y=\"-441.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"822\" y=\"-425.24\" font-family=\"Times,serif\" font-size=\"14.00\">_LKJCholeskyCov</text>\n",
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       "<!-- chol&#45;&gt;alpha -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>chol&#45;&gt;alpha</title>\n",
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       "<title>p&#45;&gt;y_cat</title>\n",
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      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x3ac0f2c90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coords = {\n",
    "    \"alts_intercepts\": [\"ec\", \"er\", \"gc\", \"gr\"],\n",
    "    \"alts_probs\": [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
    "    \"obs\": range(N),\n",
    "}\n",
    "with pm.Model(coords=coords) as model_3:\n",
    "    ## Add data to experiment with changes later.\n",
    "    ic_ec = pm.Data(\"ic_ec\", wide_heating_df[\"ic.ec\"])\n",
    "    oc_ec = pm.Data(\"oc_ec\", wide_heating_df[\"oc.ec\"])\n",
    "    ic_er = pm.Data(\"ic_er\", wide_heating_df[\"ic.er\"])\n",
    "    oc_er = pm.Data(\"oc_er\", wide_heating_df[\"oc.er\"])\n",
    "\n",
    "    beta_ic = pm.Normal(\"beta_ic\", 0, 1)\n",
    "    beta_oc = pm.Normal(\"beta_oc\", 0, 1)\n",
    "    beta_income = pm.Normal(\"beta_income\", 0, 1, dims=\"alts_intercepts\")\n",
    "    chol, corr, stds = pm.LKJCholeskyCov(\n",
    "        \"chol\", n=4, eta=2.0, sd_dist=pm.Exponential.dist(1.0, shape=4)\n",
    "    )\n",
    "    alphas = pm.MvNormal(\"alpha\", mu=0, chol=chol, dims=\"alts_intercepts\")\n",
    "\n",
    "    u0 = alphas[0] + beta_ic * ic_ec + beta_oc * oc_ec + beta_income[0] * wide_heating_df[\"income\"]\n",
    "    u1 = alphas[1] + beta_ic * ic_er + beta_oc * oc_er + beta_income[1] * wide_heating_df[\"income\"]\n",
    "    u2 = (\n",
    "        alphas[2]\n",
    "        + beta_ic * wide_heating_df[\"ic.gc\"]\n",
    "        + beta_oc * wide_heating_df[\"oc.gc\"]\n",
    "        + beta_income[2] * wide_heating_df[\"income\"]\n",
    "    )\n",
    "    u3 = (\n",
    "        alphas[3]\n",
    "        + beta_ic * wide_heating_df[\"ic.gr\"]\n",
    "        + beta_oc * wide_heating_df[\"oc.gr\"]\n",
    "        + beta_income[3] * wide_heating_df[\"income\"]\n",
    "    )\n",
    "    u4 = np.zeros(N)  # pivot\n",
    "    s = pm.math.stack([u0, u1, u2, u3, u4]).T\n",
    "\n",
    "    p_ = pm.Deterministic(\"p\", pm.math.softmax(s, axis=1), dims=(\"obs\", \"alts_probs\"))\n",
    "    choice_obs = pm.Categorical(\"y_cat\", p=p_, observed=observed, dims=\"obs\")\n",
    "\n",
    "    idata_m3 = pm.sample_prior_predictive()\n",
    "    idata_m3.extend(\n",
    "        pm.sample(nuts_sampler=\"numpyro\", idata_kwargs={\"log_likelihood\": True}, random_seed=100)\n",
    "    )\n",
    "    idata_m3.extend(pm.sample_posterior_predictive(idata_m3))\n",
    "\n",
    "\n",
    "pm.model_to_graphviz(model_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1011,
       "width": 2011
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(20, 10))\n",
    "ax = axs[0]\n",
    "counts = wide_heating_df.groupby(\"depvar\")[\"idcase\"].count()\n",
    "predicted_shares = idata_m3[\"posterior\"][\"p\"].mean(dim=[\"chain\", \"draw\", \"obs\"])\n",
    "ci_lb = idata_m3[\"posterior\"][\"p\"].quantile(0.025, dim=[\"chain\", \"draw\", \"obs\"])\n",
    "ci_ub = idata_m3[\"posterior\"][\"p\"].quantile(0.975, dim=[\"chain\", \"draw\", \"obs\"])\n",
    "\n",
    "ax.scatter(ci_lb, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
    "ax.scatter(ci_ub, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
    "ax.scatter(\n",
    "    counts / counts.sum(),\n",
    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
    "    label=\"Observed Shares\",\n",
    "    color=\"red\",\n",
    "    s=100,\n",
    ")\n",
    "ax.hlines(\n",
    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], ci_lb, ci_ub, label=\"Predicted 95% Interval\", color=\"black\"\n",
    ")\n",
    "ax.legend()\n",
    "ax.set_title(\"Observed V Predicted Shares\")\n",
    "az.plot_ppc(idata_m3, ax=axs[1])\n",
    "axs[1].set_title(\"Posterior Predictive Checks\")\n",
    "ax.set_xlabel(\"Shares\")\n",
    "ax.set_ylabel(\"Heating System\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gzheng/projects/professional/likert_dualresponse/.venv/lib/python3.11/site-packages/arviz/stats/diagnostics.py:592: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beta_income[ec]</th>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>-0.1194</td>\n",
       "      <td>0.2745</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>125.5245</td>\n",
       "      <td>1785.3613</td>\n",
       "      <td>1.0326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_income[er]</th>\n",
       "      <td>0.0568</td>\n",
       "      <td>0.0985</td>\n",
       "      <td>-0.1462</td>\n",
       "      <td>0.2349</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>814.4173</td>\n",
       "      <td>1351.8588</td>\n",
       "      <td>1.1603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_income[gc]</th>\n",
       "      <td>0.0701</td>\n",
       "      <td>0.0810</td>\n",
       "      <td>-0.0908</td>\n",
       "      <td>0.2230</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>514.8539</td>\n",
       "      <td>1447.0504</td>\n",
       "      <td>1.0928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_income[gr]</th>\n",
       "      <td>-0.0182</td>\n",
       "      <td>0.0991</td>\n",
       "      <td>-0.2034</td>\n",
       "      <td>0.1530</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>43.2790</td>\n",
       "      <td>1394.2747</td>\n",
       "      <td>1.0660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_ic</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>21.2834</td>\n",
       "      <td>1025.6984</td>\n",
       "      <td>1.1204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_oc</th>\n",
       "      <td>-0.0037</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>-0.0063</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>130.7170</td>\n",
       "      <td>1819.1965</td>\n",
       "      <td>1.0273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[ec]</th>\n",
       "      <td>0.9330</td>\n",
       "      <td>1.0135</td>\n",
       "      <td>-0.4580</td>\n",
       "      <td>2.9612</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.0780</td>\n",
       "      <td>361.4979</td>\n",
       "      <td>1033.9501</td>\n",
       "      <td>1.0793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[er]</th>\n",
       "      <td>1.1878</td>\n",
       "      <td>1.0135</td>\n",
       "      <td>-0.3409</td>\n",
       "      <td>3.1845</td>\n",
       "      <td>0.0761</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>377.7638</td>\n",
       "      <td>1058.1381</td>\n",
       "      <td>1.1560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[gc]</th>\n",
       "      <td>2.2653</td>\n",
       "      <td>0.7507</td>\n",
       "      <td>1.2054</td>\n",
       "      <td>3.7209</td>\n",
       "      <td>0.1258</td>\n",
       "      <td>0.0897</td>\n",
       "      <td>46.5848</td>\n",
       "      <td>959.9773</td>\n",
       "      <td>1.0614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[gr]</th>\n",
       "      <td>1.0156</td>\n",
       "      <td>0.9110</td>\n",
       "      <td>-0.1379</td>\n",
       "      <td>2.6814</td>\n",
       "      <td>0.2079</td>\n",
       "      <td>0.1493</td>\n",
       "      <td>17.7565</td>\n",
       "      <td>40.8513</td>\n",
       "      <td>1.1541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[0, 0]</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4000.0000</td>\n",
       "      <td>4000.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[0, 1]</th>\n",
       "      <td>0.1392</td>\n",
       "      <td>0.3365</td>\n",
       "      <td>-0.5441</td>\n",
       "      <td>0.7382</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.0220</td>\n",
       "      <td>2135.5951</td>\n",
       "      <td>2551.8261</td>\n",
       "      <td>1.1486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[0, 2]</th>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.3488</td>\n",
       "      <td>-0.5012</td>\n",
       "      <td>0.7344</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.0419</td>\n",
       "      <td>47.6418</td>\n",
       "      <td>1379.3299</td>\n",
       "      <td>1.0662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[0, 3]</th>\n",
       "      <td>0.0536</td>\n",
       "      <td>0.3764</td>\n",
       "      <td>-0.5126</td>\n",
       "      <td>0.7631</td>\n",
       "      <td>0.0860</td>\n",
       "      <td>0.0618</td>\n",
       "      <td>24.2892</td>\n",
       "      <td>2253.6964</td>\n",
       "      <td>1.1096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[1, 0]</th>\n",
       "      <td>0.1392</td>\n",
       "      <td>0.3365</td>\n",
       "      <td>-0.5441</td>\n",
       "      <td>0.7382</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.0220</td>\n",
       "      <td>2135.5951</td>\n",
       "      <td>2551.8261</td>\n",
       "      <td>1.1486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[1, 1]</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>38.4914</td>\n",
       "      <td>2944.7704</td>\n",
       "      <td>1.0696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[1, 2]</th>\n",
       "      <td>0.1438</td>\n",
       "      <td>0.3286</td>\n",
       "      <td>-0.4194</td>\n",
       "      <td>0.8010</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>199.9289</td>\n",
       "      <td>2081.5502</td>\n",
       "      <td>1.0314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[1, 3]</th>\n",
       "      <td>0.0821</td>\n",
       "      <td>0.3555</td>\n",
       "      <td>-0.4699</td>\n",
       "      <td>0.7795</td>\n",
       "      <td>0.0605</td>\n",
       "      <td>0.0431</td>\n",
       "      <td>45.2660</td>\n",
       "      <td>2019.6281</td>\n",
       "      <td>1.0575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[2, 0]</th>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.3488</td>\n",
       "      <td>-0.5012</td>\n",
       "      <td>0.7344</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.0419</td>\n",
       "      <td>47.6418</td>\n",
       "      <td>1379.3299</td>\n",
       "      <td>1.0662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[2, 1]</th>\n",
       "      <td>0.1438</td>\n",
       "      <td>0.3286</td>\n",
       "      <td>-0.4194</td>\n",
       "      <td>0.8010</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>199.9289</td>\n",
       "      <td>2081.5502</td>\n",
       "      <td>1.0314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[2, 2]</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>21.0671</td>\n",
       "      <td>3321.0770</td>\n",
       "      <td>1.1213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[2, 3]</th>\n",
       "      <td>0.0885</td>\n",
       "      <td>0.4025</td>\n",
       "      <td>-0.5738</td>\n",
       "      <td>0.6999</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.0924</td>\n",
       "      <td>12.2708</td>\n",
       "      <td>4.3794</td>\n",
       "      <td>1.2387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[3, 0]</th>\n",
       "      <td>0.0536</td>\n",
       "      <td>0.3764</td>\n",
       "      <td>-0.5126</td>\n",
       "      <td>0.7631</td>\n",
       "      <td>0.0860</td>\n",
       "      <td>0.0618</td>\n",
       "      <td>24.2892</td>\n",
       "      <td>2253.6964</td>\n",
       "      <td>1.1096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[3, 1]</th>\n",
       "      <td>0.0821</td>\n",
       "      <td>0.3555</td>\n",
       "      <td>-0.4699</td>\n",
       "      <td>0.7795</td>\n",
       "      <td>0.0605</td>\n",
       "      <td>0.0431</td>\n",
       "      <td>45.2660</td>\n",
       "      <td>2019.6281</td>\n",
       "      <td>1.0575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[3, 2]</th>\n",
       "      <td>0.0885</td>\n",
       "      <td>0.4025</td>\n",
       "      <td>-0.5738</td>\n",
       "      <td>0.6999</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.0924</td>\n",
       "      <td>12.2708</td>\n",
       "      <td>4.3794</td>\n",
       "      <td>1.2387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol_corr[3, 3]</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3464.6160</td>\n",
       "      <td>3626.8755</td>\n",
       "      <td>1.0597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean      sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "beta_income[ec]  0.1090  0.1045 -0.1194   0.2745     0.0111   0.0079   \n",
       "beta_income[er]  0.0568  0.0985 -0.1462   0.2349     0.0034   0.0081   \n",
       "beta_income[gc]  0.0701  0.0810 -0.0908   0.2230     0.0039   0.0028   \n",
       "beta_income[gr] -0.0182  0.0991 -0.2034   0.1530     0.0166   0.0119   \n",
       "beta_ic          0.0005  0.0007 -0.0008   0.0016     0.0002   0.0001   \n",
       "beta_oc         -0.0037  0.0014 -0.0063  -0.0008     0.0001   0.0001   \n",
       "alpha[ec]        0.9330  1.0135 -0.4580   2.9612     0.1099   0.0780   \n",
       "alpha[er]        1.1878  1.0135 -0.3409   3.1845     0.0761   0.0608   \n",
       "alpha[gc]        2.2653  0.7507  1.2054   3.7209     0.1258   0.0897   \n",
       "alpha[gr]        1.0156  0.9110 -0.1379   2.6814     0.2079   0.1493   \n",
       "chol_corr[0, 0]  1.0000  0.0000  1.0000   1.0000     0.0000   0.0000   \n",
       "chol_corr[0, 1]  0.1392  0.3365 -0.5441   0.7382     0.0075   0.0220   \n",
       "chol_corr[0, 2]  0.0750  0.3488 -0.5012   0.7344     0.0588   0.0419   \n",
       "chol_corr[0, 3]  0.0536  0.3764 -0.5126   0.7631     0.0860   0.0618   \n",
       "chol_corr[1, 0]  0.1392  0.3365 -0.5441   0.7382     0.0075   0.0220   \n",
       "chol_corr[1, 1]  1.0000  0.0000  1.0000   1.0000     0.0000   0.0000   \n",
       "chol_corr[1, 2]  0.1438  0.3286 -0.4194   0.8010     0.0223   0.0344   \n",
       "chol_corr[1, 3]  0.0821  0.3555 -0.4699   0.7795     0.0605   0.0431   \n",
       "chol_corr[2, 0]  0.0750  0.3488 -0.5012   0.7344     0.0588   0.0419   \n",
       "chol_corr[2, 1]  0.1438  0.3286 -0.4194   0.8010     0.0223   0.0344   \n",
       "chol_corr[2, 2]  1.0000  0.0000  1.0000   1.0000     0.0000   0.0000   \n",
       "chol_corr[2, 3]  0.0885  0.4025 -0.5738   0.6999     0.1268   0.0924   \n",
       "chol_corr[3, 0]  0.0536  0.3764 -0.5126   0.7631     0.0860   0.0618   \n",
       "chol_corr[3, 1]  0.0821  0.3555 -0.4699   0.7795     0.0605   0.0431   \n",
       "chol_corr[3, 2]  0.0885  0.4025 -0.5738   0.6999     0.1268   0.0924   \n",
       "chol_corr[3, 3]  1.0000  0.0000  1.0000   1.0000     0.0000   0.0000   \n",
       "\n",
       "                  ess_bulk   ess_tail   r_hat  \n",
       "beta_income[ec]   125.5245  1785.3613  1.0326  \n",
       "beta_income[er]   814.4173  1351.8588  1.1603  \n",
       "beta_income[gc]   514.8539  1447.0504  1.0928  \n",
       "beta_income[gr]    43.2790  1394.2747  1.0660  \n",
       "beta_ic            21.2834  1025.6984  1.1204  \n",
       "beta_oc           130.7170  1819.1965  1.0273  \n",
       "alpha[ec]         361.4979  1033.9501  1.0793  \n",
       "alpha[er]         377.7638  1058.1381  1.1560  \n",
       "alpha[gc]          46.5848   959.9773  1.0614  \n",
       "alpha[gr]          17.7565    40.8513  1.1541  \n",
       "chol_corr[0, 0]  4000.0000  4000.0000     NaN  \n",
       "chol_corr[0, 1]  2135.5951  2551.8261  1.1486  \n",
       "chol_corr[0, 2]    47.6418  1379.3299  1.0662  \n",
       "chol_corr[0, 3]    24.2892  2253.6964  1.1096  \n",
       "chol_corr[1, 0]  2135.5951  2551.8261  1.1486  \n",
       "chol_corr[1, 1]    38.4914  2944.7704  1.0696  \n",
       "chol_corr[1, 2]   199.9289  2081.5502  1.0314  \n",
       "chol_corr[1, 3]    45.2660  2019.6281  1.0575  \n",
       "chol_corr[2, 0]    47.6418  1379.3299  1.0662  \n",
       "chol_corr[2, 1]   199.9289  2081.5502  1.0314  \n",
       "chol_corr[2, 2]    21.0671  3321.0770  1.1213  \n",
       "chol_corr[2, 3]    12.2708     4.3794  1.2387  \n",
       "chol_corr[3, 0]    24.2892  2253.6964  1.1096  \n",
       "chol_corr[3, 1]    45.2660  2019.6281  1.0575  \n",
       "chol_corr[3, 2]    12.2708     4.3794  1.2387  \n",
       "chol_corr[3, 3]  3464.6160  3626.8755  1.0597  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(\n",
    "    idata_m3, var_names=[\"beta_income\", \"beta_ic\", \"beta_oc\", \"alpha\", \"chol_corr\"], round_to=4\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-13.242388002273604"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "post = az.extract(idata_m3)\n",
    "substitution_rate = post[\"beta_oc\"] / post[\"beta_ic\"]\n",
    "substitution_rate.mean().item()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tweaks\n",
    "\n",
    "My preferred model is model 2, but with\n",
    "- a control for income\n",
    "- (ideally) set-constrained coefficients for installation cost and operating cost. However, this doesn't work. In practice it's because the coefficients are both so small (especially the $\\beta_\\text{installation cost}$) that constraining it is difficult\n",
    "\n",
    "Because they're so small and so close to zero, it's also not meaningful to talk about a MRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alpha, beta_ic, beta_income, beta_oc, y_cat]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "116ff0a546e244f9b1f4ea040e78cb81",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75fa02a612dc413eb3140eb6e73b5455",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77c5b0cf9ff04d9085c6b3ed046e35be",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
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     "text": [
      "Sampling: [y_cat]\n"
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       "Output()"
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   "source": [
    "coords = {\n",
    "    \"alts_intercepts\": [\"ec\", \"er\", \"gc\", \"gr\"],\n",
    "    \"alts_probs\": [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
    "    \"obs\": range(N),\n",
    "}\n",
    "\n",
    "with pm.Model(coords=coords) as model_4:\n",
    "    beta_ic = pm.Normal(\"beta_ic\")\n",
    "    beta_oc = pm.Normal(\"beta_oc\")\n",
    "    beta_income = pm.Normal(\"beta_income\", 0, 1, dims=\"alts_intercepts\")\n",
    "    alphas = pm.Normal(\"alpha\", 0, 5, dims=\"alts_intercepts\")\n",
    "\n",
    "    ## Construct Utility matrix and Pivot using an intercept per alternative\n",
    "    u0 = alphas[0] + beta_ic * wide_heating_df[\"ic.ec\"] + beta_oc * wide_heating_df[\"oc.ec\"] + beta_income[0] * wide_heating_df[\"income\"]\n",
    "    u1 = alphas[1] + beta_ic * wide_heating_df[\"ic.er\"] + beta_oc * wide_heating_df[\"oc.er\"] + beta_income[1] * wide_heating_df[\"income\"]\n",
    "    u2 = alphas[2] + beta_ic * wide_heating_df[\"ic.gc\"] + beta_oc * wide_heating_df[\"oc.gc\"] + beta_income[2] * wide_heating_df[\"income\"]\n",
    "    u3 = alphas[3] + beta_ic * wide_heating_df[\"ic.gr\"] + beta_oc * wide_heating_df[\"oc.gr\"] + beta_income[3] * wide_heating_df[\"income\"]\n",
    "    u4 = np.zeros(N)  # Outside Good\n",
    "    s = pm.math.stack([u0, u1, u2, u3, u4]).T\n",
    "\n",
    "    ## Apply Softmax Transform\n",
    "    p_ = pm.Deterministic(\"p\", pm.math.softmax(s, axis=1), dims=(\"obs\", \"alts_probs\"))\n",
    "\n",
    "    ## Likelihood\n",
    "    choice_obs = pm.Categorical(\"y_cat\", p=p_, observed=observed, dims=\"obs\")\n",
    "\n",
    "    idata_m4 = pm.sample_prior_predictive()\n",
    "    idata_m4.extend(\n",
    "        pm.sample(nuts_sampler=\"numpyro\", idata_kwargs={\"log_likelihood\": True}, random_seed=100)\n",
    "    )\n",
    "    idata_m4.extend(pm.sample_posterior_predictive(idata_m4))\n",
    "\n",
    "\n",
    "pm.model_to_graphviz(model_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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6d++eK6+8cp4XHwDzVl9q3gkWqBv1peaBulOOun/jjTeK427dui3w/sDCq8+f9ddcc03atWuX3XffvWQxgIVTn987AABgUTRq1CjHHnts2rRpk2222SY77bTTXOfPeP1J165dU1npsmeA2vbiiy/mySefTNOmTXP11Vena9eu5U4JYLE3duzYXHvttTn++OPLnQoA1FsNyp0ApTHjye4GDebvf+aqqqri+LvvvlvgmGPGjCmO5/cE+/cxJ02atMDxgB+Uo+aB8ilHzTdv3jzXX3/9fM395JNP8uGHHyaZflJys802W+B4wMzq22d9TU1Nhg0bliuuuCLPPvtsDjzwwPzhD3+Y6QJaYOHVh5ofM2ZMrr322txyyy2LfCxg7upDzdfU1KRfv3556KGH8u2336ampibjx4/Puuuumz333DMbbbTRIscAflCOun/33XeL45YtWyZJJk6cmKuuuiqvvfZapk6dmrFjx+ZHP/pRdtxxx+y4447znRswd/Xhs352Pvzww9xwww15+OGH3agO6qH6+t4BAAC14aCDDspBBx00X3OfeOKJ4nibbbYpVUoAS6WxY8fmsccey3nnnZeNN944f/zjH7P66quXOy2AJcLFF1+cQw45JK1atSp3KgBLhX//+9/p27dv3nnnnVRVVWXMmDFp165dtttuu+yzzz6uca+nXJm0hJqx0Xx+T3bPOG/y5MkLHHPGE+wznjifG83tUDvKUfNA+dT3mr/77ruL44MOOiidO3cuaTxYGtSXur/00kvz3nvvZciQIfniiy/Srl27XHvttVl//fVdCA+1qD7U/J///GcnWKCOlLvmBw8enF69eqVr16455ZRTiisxjB8/Pn/+859z4IEHZvvtt895552XZZZZZpFiAdOVo+5HjRpVHLdo0SLDhw9Pnz59csABB+T4449PZWVlpk6dmvvvvz9nnHFGrr/++lx++eVZZZVVFjgWMLNyf9bPTqFQyOmnn5699947HTt2rPXjA4uuPr53AABAXfvoo48ycODAJEnnzp1zwAEHlDkjgMXf888/n/vuuy9Dhw7N0KFDM3HixBxzzDH5zW9+o+EHoJa8+eabGT58eH75y1+WOxWAJV51dXUuueSS/POf/8yRRx6Z008/PQ0bNkxNTU2eeuqpnHrqqbnlllty+eWXZ6211ip3uvwXze1LqHHjxhXH89toXllZWRwvzJ3cNbdD+ZSj5oHyqc81P2LEiNx5551Jpp9Y/N3vfleyWLA0qS91f+yxxxbH06ZNy2uvvZYzzzwzkydPzlFHHZWddtqpVuLA0q7cNd+/f38nWKAOlbvmH3nkkVx22WWzrHjTvHnznHbaafnmm2/y6KOP5ttvv82NN95oJWeoBeWo+xmb25dZZpmcddZZOfPMM2e6IV2DBg3Ss2fPLL/88vnd736XvffeO3fccUd+9KMfLXA84Afl/qyfnRdeeCEDBgzIBRdcUOvHBmpHfXzvAACAunbFFVcUx717907jxo3LmA3AkmGzzTbLZpttVnw8bNiw/P3vf8+GG26YXXbZJSeeeGKaNWtWxgwBFm/V1dU599xzc+mll1owCKAOfP311xk2bFhuv/32NGrUqPh8ZWVlttlmm1RVVeX3v/99evXqldtuuy1rrLFGGbPlv1XOewqLo5qamtmO53efQqGwwDFn3Gd+9/8+5sLEA35QjpoHyqc+1/y5556bCRMmpG3btrnhhhv8oRtqSX2s+6qqqmy00Ua57bbbUlVVlRNOOCEnn3xyJk6cWOuxYGlTzpr//gTL6aef7gQL1JFy1vwaa6yRSy65ZJbG9u9VVFSkd+/eSZLXX38911577ULHAn5Qjrqf8Qazzz77bLp37z5TY/uMNttss2y88cYZM2ZMjj766EydOnWB4wE/qG+/0xcKhVx55ZXp2rVrVlpppVo9NlB76tt7BwAA1LXnn38+jz/+eJLpje1bbLFFmTMCWDKtuOKKOfPMM9OrV6/cfvvt2X333fPvf/+73GkBLLauv/76bLPNNs7BANSB1q1bp1evXrngggtmamyf0VZbbZWNN944EydOzLHHHpspU6bUcZbMjeb2JVSLFi2K44U52d20adNFijlt2rQFirnMMssscDzgB+WoeaB86mvNP/jgg3nsscfSvHnzXHfddVl55ZVLEgeWRvW17pPpuR1++OFJkvvuuy/nnntuyWLB0qKcNX/99ddn2223dYIF6lA5a37rrbfO9ttvP9c57du3L67afP3112fMmDELHQ+Yrhx137x58+J4yJAh2WOPPeY6v0ePHkmSjz76KA888MACxwN+UN9+p3/hhRfy9ttvZ/PNN6/V4wK1q769dwAAQF365ptv0qdPnyTJEUcckV69epU5I4Al32GHHZbmzZtn8ODBOfjggzNq1KhypwSw2BkyZEiefPLJHHjggeVOBWCp0Lx58/Tu3XuOje3f+/4amE8//TT9+vWri9SYT5rbl1AtW7Ysjud3VZUZ5y1Ms/mMMee3uf37mJrbYdGUo+aB8qmPNf/++++nT58+adasWa699tr85Cc/qfUYsDSrj3U/o+222y6tW7dOktx999154YUXShoPlnTlqvnBgwc7wQJlUN8/55Nk3XXXTZJMmDAhzz77bMnjwZKuHHU/Y4Pc6quvnuWWW26u8//nf/6nOH7ooYcWOB7wg/r2WX/vvfcmSdZaa61aPS5Qu+rbewcAANSVKVOm5JhjjsmIESNy4IEH5qijjip3SgBLhaZNm2bXXXdNMv0mI2eeeWZ5EwJYzBQKhfzpT3/KqaeeOs8mSwDqVvfu3YvjBx98sIyZ8N80ty+hFvVkd5MmTRY45owXxy1oTCfXYdGUo+aB8qlvNT9ixIj8/ve/zzLLLJNbb70166+/fq0eH6h/df/fGjVqNNNF8X/9619LGg+WdOWoeSdYoHzq++d8krRq1ao47t+/f8njwZKuHHU/48rtK6ywwjznf3/zqmR63c/virHArOrTZ/2ECRPy/PPPJ0lWWWWVWjsuUPvq03sHAADUlUKhkDPOOCP9+/fP0Ucfnf/3//5fKioqyp0WwFJjxqafRx55JIMHDy5fMgCLmX79+qVz585Zb731yp0KAP9lxvNuAwYMcA1MPaK5fQk1Y6P5qFGj5mufkSNHFsfLLrvsAsecsdDnJ2Z1dXXGjRu30PGAH5Sj5oHyqU81P378+Pzud79LTU1N+vbtmzXXXLPWjg38oFx1P3jw4PTv3z/Tpk2b59zll1++OB44cGCqq6sXKiZQnprv169fVlxxRSdYoAzKUfNfffVV9t1332y22Wa566675jm/cePGxfGIESMWOB4ws3LUfdeuXWcbf05mrPspU6Zk9OjRCxwTmK4+/S3vueeey6RJk5IkXbp0qbXjArWvPr13AABAXbnqqqtyzz33pHfv3jniiCM0tgPUgnHjxuXVV1+d6e8Gc9K+ffuZHr/xxhulSgtgiTJq1Khcf/31Of7448udCsBS46KLLkqPHj1yzDHHzPOa9RlvCj1lypT5PvdG6WluX0K1bdu2uBLLV199NV/7fD+vcePGWWmllRY45owXwcxPzK+//ro4Xm211RY4HvCDctQ8UD71pearq6tz9NFH57vvvsvtt98+04XyQO0qR90/+OCD2XbbbfOrX/0qRxxxxDznL7fccsVxoVDQ/AKLoK5rfuTIkbnuuuucYIEyKcfn/KOPPpoBAwbkyy+/zK233jrP+VOmTCmOGzZsuMDxgJmVo+7XWmut4njs2LELvH9VVdUC7wNMV1/+lpdM/w6QTG96XWaZZWrtuEDtq0/vHQAAUBfuu+++XH311bngggvSq1evcqcDsEQYOXJkdthhhxxwwAHZZpttMmTIkLnOb9u27Sz7AzBvF154YQ499NC0atWq3KkALBXGjh2b6667LqNGjcpjjz2Wjz76aK7zZ7z2LXH9W33SoNwJUBpVVVXp3r17Xnzxxfk+2f19s/nqq6++UBeq/fSnPy2O52cFpxmb29dYY40Fjgf8oBw1D5RPfaj5QqGQPn365Msvv8w//vGPmVZsntHYsWMzYMCAbL755oscE5Zm5aj7hx9+uDh+9tlnM3r06LRu3XqO87/77ruZHs9tLjB3dV3zr732WqZMmZJDDjlkrvOmTp1aHD/33HPZa6+9io979uyZnj17LlBcYLpyfM43a9YsDRs2TOvWrfPrX/96nvPHjx9fHHfq1GmB4wEzK0fdr7baamnSpEkmTZo0X3eg/n5l5yRp2rRpWrZsucAxgenqw9/ykqSmpib//Oc/k0yva6B+qy/vHQAAUBdeeuml/OlPf8oll1yS7bbbbo7znnzyyWyyySZu2AYwn15//fXi3xXGjRuXZ555JgceeOAc5894biBJ2rRpU9L8AJYUL7/8cj7++OPccsstc5336aefFsczXnfVrl27XHXVVSXLD2BJ07hx4zRo0CCNGzfOhhtumG7dus11/ozXvjVv3jwtWrQodYrMJ83tS7AtttgiL774YkaNGpUxY8bM9S5AkydPzhdffJEk2WabbRYqXrt27bL22mvnnXfemeed3ZJk8ODBSZLKyspsvfXWCxUT+EFd1zxQXuWu+csuuyz//ve/c+utt85yx9YZDRw4ME899ZTmdqgFdV33M14Q0LZt23n+Ij/jRbZrr712GjVqtFBxgenqsua33377bL/99vOc99lnn2WrrbZKkmy++eY5//zzFzgWMHt1/Tm/xhprpFu3brn//vvna/6MN6n8+c9/vlAxgZnVdd03atQo2223XR544IH5apCb8cRejx49UlFRsVBxgenK/be8JBk2bFgmTpyYRHM7LC7qw3sHAACU2r///e8cf/zxueSSS4rnoWanUCjknHPOydNPP12H2QEs3v77ZiCrrLLKXOf/9/mDGRe+A2DOnn/++fma16tXr7z++utJkrvuuquUKQEs0Ro3bpwf/ehHueiii7L66qvPc/5/X/vmGpj6o7LcCVA6u+y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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1011,
       "width": 2011
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(20, 10))\n",
    "ax = axs[0]\n",
    "counts = wide_heating_df.groupby(\"depvar\")[\"idcase\"].count()\n",
    "predicted_shares = idata_m4[\"posterior\"][\"p\"].mean(dim=[\"chain\", \"draw\", \"obs\"])\n",
    "ci_lb = idata_m4[\"posterior\"][\"p\"].quantile(0.025, dim=[\"chain\", \"draw\", \"obs\"])\n",
    "ci_ub = idata_m4[\"posterior\"][\"p\"].quantile(0.975, dim=[\"chain\", \"draw\", \"obs\"])\n",
    "\n",
    "ax.scatter(ci_lb, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
    "ax.scatter(ci_ub, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
    "ax.scatter(\n",
    "    counts / counts.sum(),\n",
    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
    "    label=\"Observed Shares\",\n",
    "    color=\"red\",\n",
    "    s=100,\n",
    ")\n",
    "ax.hlines(\n",
    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], ci_lb, ci_ub, label=\"Predicted 95% Interval\", color=\"black\"\n",
    ")\n",
    "ax.legend()\n",
    "ax.set_title(\"Observed V Predicted Shares\")\n",
    "az.plot_ppc(idata_m4, ax=axs[1])\n",
    "axs[1].set_title(\"Posterior Predictive Checks\")\n",
    "ax.set_xlabel(\"Shares\")\n",
    "ax.set_ylabel(\"Heating System\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beta_ic</th>\n",
       "      <td>-0.0007</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>-0.0018</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2034.8098</td>\n",
       "      <td>2619.1608</td>\n",
       "      <td>1.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_oc</th>\n",
       "      <td>-0.0055</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>-0.0081</td>\n",
       "      <td>-0.0025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2549.3952</td>\n",
       "      <td>3052.4259</td>\n",
       "      <td>1.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_income[ec]</th>\n",
       "      <td>-0.0415</td>\n",
       "      <td>0.1123</td>\n",
       "      <td>-0.2552</td>\n",
       "      <td>0.1597</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>1233.5860</td>\n",
       "      <td>1949.2437</td>\n",
       "      <td>1.0024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_income[er]</th>\n",
       "      <td>-0.0708</td>\n",
       "      <td>0.1064</td>\n",
       "      <td>-0.2544</td>\n",
       "      <td>0.1412</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>1151.0759</td>\n",
       "      <td>1873.4452</td>\n",
       "      <td>1.0022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_income[gc]</th>\n",
       "      <td>-0.0527</td>\n",
       "      <td>0.0866</td>\n",
       "      <td>-0.2195</td>\n",
       "      <td>0.1042</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>1063.0967</td>\n",
       "      <td>1595.1725</td>\n",
       "      <td>1.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_income[gr]</th>\n",
       "      <td>-0.1619</td>\n",
       "      <td>0.0990</td>\n",
       "      <td>-0.3460</td>\n",
       "      <td>0.0231</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>1134.4858</td>\n",
       "      <td>1809.5429</td>\n",
       "      <td>1.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[ec]</th>\n",
       "      <td>3.5846</td>\n",
       "      <td>0.9338</td>\n",
       "      <td>1.9701</td>\n",
       "      <td>5.4876</td>\n",
       "      <td>0.0286</td>\n",
       "      <td>0.0204</td>\n",
       "      <td>1066.7882</td>\n",
       "      <td>1428.9757</td>\n",
       "      <td>1.0018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[er]</th>\n",
       "      <td>3.8573</td>\n",
       "      <td>0.9128</td>\n",
       "      <td>2.1368</td>\n",
       "      <td>5.5381</td>\n",
       "      <td>0.0294</td>\n",
       "      <td>0.0210</td>\n",
       "      <td>968.1931</td>\n",
       "      <td>1275.9228</td>\n",
       "      <td>1.0013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[gc]</th>\n",
       "      <td>4.1892</td>\n",
       "      <td>0.6397</td>\n",
       "      <td>2.9483</td>\n",
       "      <td>5.3714</td>\n",
       "      <td>0.0211</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>918.4178</td>\n",
       "      <td>1199.7850</td>\n",
       "      <td>1.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[gr]</th>\n",
       "      <td>3.1823</td>\n",
       "      <td>0.7224</td>\n",
       "      <td>1.8973</td>\n",
       "      <td>4.5941</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>967.8811</td>\n",
       "      <td>1118.1689</td>\n",
       "      <td>1.0017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean      sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "beta_ic         -0.0007  0.0006 -0.0018   0.0004     0.0000   0.0000   \n",
       "beta_oc         -0.0055  0.0015 -0.0081  -0.0025     0.0000   0.0000   \n",
       "beta_income[ec] -0.0415  0.1123 -0.2552   0.1597     0.0032   0.0023   \n",
       "beta_income[er] -0.0708  0.1064 -0.2544   0.1412     0.0031   0.0022   \n",
       "beta_income[gc] -0.0527  0.0866 -0.2195   0.1042     0.0027   0.0019   \n",
       "beta_income[gr] -0.1619  0.0990 -0.3460   0.0231     0.0029   0.0021   \n",
       "alpha[ec]        3.5846  0.9338  1.9701   5.4876     0.0286   0.0204   \n",
       "alpha[er]        3.8573  0.9128  2.1368   5.5381     0.0294   0.0210   \n",
       "alpha[gc]        4.1892  0.6397  2.9483   5.3714     0.0211   0.0150   \n",
       "alpha[gr]        3.1823  0.7224  1.8973   4.5941     0.0232   0.0165   \n",
       "\n",
       "                  ess_bulk   ess_tail   r_hat  \n",
       "beta_ic          2034.8098  2619.1608  1.0010  \n",
       "beta_oc          2549.3952  3052.4259  1.0023  \n",
       "beta_income[ec]  1233.5860  1949.2437  1.0024  \n",
       "beta_income[er]  1151.0759  1873.4452  1.0022  \n",
       "beta_income[gc]  1063.0967  1595.1725  1.0012  \n",
       "beta_income[gr]  1134.4858  1809.5429  1.0011  \n",
       "alpha[ec]        1066.7882  1428.9757  1.0018  \n",
       "alpha[er]         968.1931  1275.9228  1.0013  \n",
       "alpha[gc]         918.4178  1199.7850  1.0012  \n",
       "alpha[gr]         967.8811  1118.1689  1.0017  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(\n",
    "    idata_m4, var_names=[\"beta_ic\", \"beta_oc\", \"beta_income\", \"alpha\"], round_to=4\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.17482352077738"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "post = az.extract(idata_m4)\n",
    "substitution_rate = post[\"beta_oc\"] / post[\"beta_ic\"]\n",
    "substitution_rate.mean().item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1011,
       "width": 2011
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "ax.hist(\n",
    "    substitution_rate,\n",
    "    bins=30,\n",
    "    ec=\"black\",\n",
    ")\n",
    "ax.set_title(\"Uncertainty in Marginal Rate of Substitution \\n Operating Costs / Installation Costs\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Counterfactuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y_cat]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "724384ed57c94a83be6fcafaa9b55d2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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     },
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      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "\n",
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       ".xr-var-data-in:checked ~ .xr-var-data,\n",
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       "\n",
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       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
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       "\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 173MB\n",
       "Dimensions:     (chain: 4, draw: 1000, obs: 900, alts_probs: 5)\n",
       "Coordinates:\n",
       "  * chain       (chain) int64 32B 0 1 2 3\n",
       "  * draw        (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * obs         (obs) int64 7kB 0 1 2 3 4 5 6 7 ... 893 894 895 896 897 898 899\n",
       "  * alts_probs  (alts_probs) &lt;U2 40B &#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;\n",
       "Data variables:\n",
       "    p           (chain, draw, obs, alts_probs) float64 144MB 0.05725 ... 0.04717\n",
       "    y_cat       (chain, draw, obs) int64 29MB 2 4 4 2 1 2 2 2 ... 3 3 2 1 2 4 2\n",
       "Attributes:\n",
       "    created_at:                 2024-06-23T20:34:06.401295+00:00\n",
       "    arviz_version:              0.18.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.15.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ff6db017-572b-4988-bf1e-4f2917e5dcc6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ff6db017-572b-4988-bf1e-4f2917e5dcc6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>obs</span>: 900</li><li><span class='xr-has-index'>alts_probs</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5bf3d195-fb89-41c7-958c-92284ccbdcfe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5bf3d195-fb89-41c7-958c-92284ccbdcfe' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-6cf5a1cf-9dbc-422a-ba03-ab9c0daacde6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6cf5a1cf-9dbc-422a-ba03-ab9c0daacde6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2282a76-b91b-41fc-bf20-891e3d785ed1' class='xr-var-data-in' type='checkbox'><label for='data-b2282a76-b91b-41fc-bf20-891e3d785ed1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-6a6b1a7e-b8d9-4230-aa51-25ff24dbd389' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a6b1a7e-b8d9-4230-aa51-25ff24dbd389' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74986031-fa08-42d0-8029-e2cb7150a633' class='xr-var-data-in' type='checkbox'><label for='data-74986031-fa08-42d0-8029-e2cb7150a633' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-fc08714d-bec6-47f4-935a-bb435737a380' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc08714d-bec6-47f4-935a-bb435737a380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c116faf-e1fc-4433-a993-831c36a272bf' class='xr-var-data-in' type='checkbox'><label for='data-0c116faf-e1fc-4433-a993-831c36a272bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alts_probs</span></div><div class='xr-var-dims'>(alts_probs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</div><input id='attrs-291b0eb4-24cd-4b14-b315-6f7205cf7f07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-291b0eb4-24cd-4b14-b315-6f7205cf7f07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b026e739-0dae-477c-a124-d0cb3f0967e1' class='xr-var-data-in' type='checkbox'><label for='data-b026e739-0dae-477c-a124-d0cb3f0967e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-018ce695-7ed5-45ae-93c4-5a5af765ee2b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-018ce695-7ed5-45ae-93c4-5a5af765ee2b' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(chain, draw, obs, alts_probs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.05725 0.06145 ... 0.1894 0.04717</div><input id='attrs-45f8d977-33ba-4433-ab8a-d9512e36ec5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45f8d977-33ba-4433-ab8a-d9512e36ec5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff013801-f77b-4811-9d74-61b9c682d1e0' class='xr-var-data-in' type='checkbox'><label for='data-ff013801-f77b-4811-9d74-61b9c682d1e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[0.05724768, 0.06144754, 0.68573654, 0.12073992, 0.07482832],\n",
       "         [0.0536941 , 0.06504792, 0.66728469, 0.13613161, 0.07784168],\n",
       "         [0.06074645, 0.07880667, 0.63082648, 0.15196209, 0.0776583 ],\n",
       "         ...,\n",
       "         [0.05956621, 0.07063259, 0.65266042, 0.13936663, 0.07777414],\n",
       "         [0.06049453, 0.0836567 , 0.63028224, 0.14349363, 0.0820729 ],\n",
       "         [0.04665807, 0.07612694, 0.61031866, 0.17417927, 0.09271705]],\n",
       "\n",
       "        [[0.0575523 , 0.05957472, 0.70580507, 0.12394815, 0.05311976],\n",
       "         [0.04978846, 0.05194444, 0.70624589, 0.13173661, 0.0602846 ],\n",
       "         [0.05858354, 0.06523037, 0.65436287, 0.15864973, 0.06317349],\n",
       "         ...,\n",
       "         [0.0572611 , 0.06067699, 0.67328214, 0.15106374, 0.05771602],\n",
       "         [0.05613799, 0.08006781, 0.64611688, 0.15639254, 0.06128477],\n",
       "         [0.03569163, 0.04690307, 0.65923192, 0.18508592, 0.07308747]],\n",
       "\n",
       "        [[0.03848548, 0.06967034, 0.71777804, 0.11688472, 0.05718142],\n",
       "         [0.04106635, 0.06133217, 0.70216202, 0.12892532, 0.06651414],\n",
       "         [0.05499752, 0.08312547, 0.62147335, 0.17002871, 0.07037495],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [0.05812185, 0.07607232, 0.66578602, 0.14396336, 0.05605645],\n",
       "         [0.06088941, 0.08638921, 0.64408592, 0.14895731, 0.05967816],\n",
       "         [0.05234028, 0.06589006, 0.65931254, 0.14420871, 0.07824841]],\n",
       "\n",
       "        [[0.04946695, 0.04037203, 0.74481053, 0.10060332, 0.06474716],\n",
       "         [0.05557851, 0.04546533, 0.71803102, 0.10593543, 0.07498971],\n",
       "         [0.07094328, 0.07473905, 0.60520828, 0.16683318, 0.08227621],\n",
       "         ...,\n",
       "         [0.05864539, 0.05887554, 0.64364577, 0.17843161, 0.0604017 ],\n",
       "         [0.04773015, 0.08160192, 0.63828546, 0.17464675, 0.05773572],\n",
       "         [0.04628499, 0.05529322, 0.60885068, 0.24491774, 0.04465337]],\n",
       "\n",
       "        [[0.03588243, 0.06452423, 0.70246049, 0.13061053, 0.06652232],\n",
       "         [0.04836715, 0.064673  , 0.69243603, 0.12374824, 0.07077557],\n",
       "         [0.0621403 , 0.08466819, 0.62086437, 0.15856235, 0.07376478],\n",
       "         ...,\n",
       "         [0.04919897, 0.07558963, 0.6408676 , 0.17349437, 0.06084943],\n",
       "         [0.04264316, 0.09181579, 0.6372937 , 0.16979284, 0.05845452],\n",
       "         [0.06034545, 0.06557241, 0.6375511 , 0.18935961, 0.04717142]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(chain, draw, obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 4 4 2 1 2 2 2 ... 3 3 3 2 1 2 4 2</div><input id='attrs-b6986981-f37d-4b71-bddd-d7ee9724e320' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6986981-f37d-4b71-bddd-d7ee9724e320' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a71467a-9017-46da-a4e3-9e284d666f8a' class='xr-var-data-in' type='checkbox'><label for='data-3a71467a-9017-46da-a4e3-9e284d666f8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[2, 4, 4, ..., 3, 2, 2],\n",
       "        [1, 0, 0, ..., 2, 2, 2],\n",
       "        [2, 2, 2, ..., 2, 2, 2],\n",
       "        ...,\n",
       "        [3, 2, 1, ..., 2, 2, 2],\n",
       "        [2, 2, 2, ..., 2, 2, 2],\n",
       "        [0, 2, 2, ..., 2, 2, 2]],\n",
       "\n",
       "       [[0, 2, 2, ..., 2, 0, 2],\n",
       "        [2, 0, 2, ..., 2, 0, 1],\n",
       "        [4, 2, 2, ..., 2, 3, 3],\n",
       "        ...,\n",
       "        [2, 2, 2, ..., 2, 2, 2],\n",
       "        [2, 3, 2, ..., 3, 0, 3],\n",
       "        [2, 2, 3, ..., 2, 2, 2]],\n",
       "\n",
       "       [[2, 2, 2, ..., 2, 2, 3],\n",
       "        [2, 2, 4, ..., 2, 0, 1],\n",
       "        [2, 0, 4, ..., 2, 4, 3],\n",
       "        ...,\n",
       "        [4, 4, 2, ..., 2, 1, 2],\n",
       "        [2, 4, 1, ..., 2, 2, 2],\n",
       "        [3, 3, 3, ..., 2, 4, 2]],\n",
       "\n",
       "       [[2, 2, 3, ..., 2, 2, 2],\n",
       "        [2, 2, 2, ..., 2, 2, 1],\n",
       "        [2, 2, 2, ..., 2, 2, 2],\n",
       "        ...,\n",
       "        [2, 0, 2, ..., 2, 1, 2],\n",
       "        [2, 2, 2, ..., 3, 2, 3],\n",
       "        [2, 3, 1, ..., 2, 4, 2]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66f73139-8731-4f18-881e-54af431a7e85' class='xr-section-summary-in' type='checkbox'  ><label for='section-66f73139-8731-4f18-881e-54af431a7e85' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-60e38610-47ad-44fd-bb29-d693243b1d54' class='xr-index-data-in' type='checkbox'/><label for='index-60e38610-47ad-44fd-bb29-d693243b1d54' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c395f821-338f-428e-95e5-f8ed9532ca76' class='xr-index-data-in' type='checkbox'/><label for='index-c395f821-338f-428e-95e5-f8ed9532ca76' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9e2fcaef-d3ba-4067-8ccf-ce5e347d4a1a' class='xr-index-data-in' type='checkbox'/><label for='index-9e2fcaef-d3ba-4067-8ccf-ce5e347d4a1a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>alts_probs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c0daa62e-083e-4713-9164-ebcf09a0241b' class='xr-index-data-in' type='checkbox'/><label for='index-c0daa62e-083e-4713-9164-ebcf09a0241b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;alts_probs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-afa7ae9c-7cf9-4da6-964f-2ae85585ed91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-afa7ae9c-7cf9-4da6-964f-2ae85585ed91' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-23T20:34:06.401295+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.15.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_predictions_constant_data9c91b07b-a1b0-4429-a9ff-3e2090b8ee04\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_predictions_constant_data9c91b07b-a1b0-4429-a9ff-3e2090b8ee04\" class = \"xr-section-summary\">predictions_constant_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\n",
       "html[theme=dark],\n",
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       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
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       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
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       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
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       "\n",
       ".xr-obj-type {\n",
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       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
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       "\n",
       ".xr-section-item {\n",
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       "\n",
       ".xr-section-item input {\n",
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       "\n",
       ".xr-section-item input + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       ".xr-section-summary-in:disabled + label {\n",
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       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
       ".xr-index-preview {\n",
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       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 58kB\n",
       "Dimensions:      (ic_ec_dim_0: 900, ic_er_dim_0: 900, oc_ec_dim_0: 900,\n",
       "                  oc_er_dim_0: 900)\n",
       "Coordinates:\n",
       "  * ic_ec_dim_0  (ic_ec_dim_0) int64 7kB 0 1 2 3 4 5 ... 894 895 896 897 898 899\n",
       "  * ic_er_dim_0  (ic_er_dim_0) int64 7kB 0 1 2 3 4 5 ... 894 895 896 897 898 899\n",
       "  * oc_ec_dim_0  (oc_ec_dim_0) int64 7kB 0 1 2 3 4 5 ... 894 895 896 897 898 899\n",
       "  * oc_er_dim_0  (oc_er_dim_0) int64 7kB 0 1 2 3 4 5 ... 894 895 896 897 898 899\n",
       "Data variables:\n",
       "    ic_ec        (ic_ec_dim_0) float64 7kB 859.9 796.8 719.9 ... 799.8 967.9\n",
       "    ic_er        (ic_er_dim_0) float64 7kB 995.8 894.7 ... 1.123e+03 1.092e+03\n",
       "    oc_ec        (oc_ec_dim_0) float64 7kB 664.0 624.3 526.9 ... 594.2 622.4\n",
       "    oc_er        (oc_er_dim_0) float64 7kB 606.7 583.8 485.7 ... 481.9 550.2\n",
       "Attributes:\n",
       "    created_at:                 2024-06-23T20:34:06.404039+00:00\n",
       "    arviz_version:              0.18.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.15.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ab64f366-f6f5-4da2-97bf-c18c26db299c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ab64f366-f6f5-4da2-97bf-c18c26db299c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>ic_ec_dim_0</span>: 900</li><li><span class='xr-has-index'>ic_er_dim_0</span>: 900</li><li><span class='xr-has-index'>oc_ec_dim_0</span>: 900</li><li><span class='xr-has-index'>oc_er_dim_0</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f85304cf-2210-4a9c-84ce-e689d0d750be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f85304cf-2210-4a9c-84ce-e689d0d750be' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ic_ec_dim_0</span></div><div class='xr-var-dims'>(ic_ec_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-399d683d-50a8-4d07-a4f2-807b4594bee9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-399d683d-50a8-4d07-a4f2-807b4594bee9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-948d0f9a-48cf-44ea-8d4f-945578a1deb1' class='xr-var-data-in' type='checkbox'><label for='data-948d0f9a-48cf-44ea-8d4f-945578a1deb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ic_er_dim_0</span></div><div class='xr-var-dims'>(ic_er_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-6051b625-f783-4388-a5bf-cbefeb3fa320' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6051b625-f783-4388-a5bf-cbefeb3fa320' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3509be9f-2932-4bea-8f0a-ecb7c7eddd9b' class='xr-var-data-in' type='checkbox'><label for='data-3509be9f-2932-4bea-8f0a-ecb7c7eddd9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>oc_ec_dim_0</span></div><div class='xr-var-dims'>(oc_ec_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-c7183f01-dc56-4c7e-92e7-85e4885bb16e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7183f01-dc56-4c7e-92e7-85e4885bb16e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-019189f8-d6a2-4318-972a-9931f084c31f' class='xr-var-data-in' type='checkbox'><label for='data-019189f8-d6a2-4318-972a-9931f084c31f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>oc_er_dim_0</span></div><div class='xr-var-dims'>(oc_er_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-160f806c-ae9a-498f-85b4-ae798bd1323c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-160f806c-ae9a-498f-85b4-ae798bd1323c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f261462-72a3-4f3c-93d7-02c5aa0a8e8d' class='xr-var-data-in' type='checkbox'><label for='data-5f261462-72a3-4f3c-93d7-02c5aa0a8e8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-291d0841-b5fd-4ed3-b04d-75c9739bee7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-291d0841-b5fd-4ed3-b04d-75c9739bee7d' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ic_ec</span></div><div class='xr-var-dims'>(ic_ec_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>859.9 796.8 719.9 ... 799.8 967.9</div><input id='attrs-4903fae9-f9c3-4d91-a6fb-2e62a76ca219' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4903fae9-f9c3-4d91-a6fb-2e62a76ca219' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c07a440d-123c-4ac1-a153-ce0efaa47749' class='xr-var-data-in' type='checkbox'><label for='data-c07a440d-123c-4ac1-a153-ce0efaa47749' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 859.9 ,  796.82,  719.86,  761.25,  858.86,  693.74,  633.63,\n",
       "        813.33,  875.7 ,  775.86,  792.29,  944.35,  797.34,  687.18,\n",
       "        594.  ,  801.2 ,  821.79,  696.89,  572.3 ,  925.62,  788.07,\n",
       "        934.75,  619.65,  805.29,  943.05,  882.55,  869.65,  991.82,\n",
       "        724.01,  805.08,  838.01,  671.78, 1030.3 ,  785.95,  689.1 ,\n",
       "        918.92,  618.3 ,  891.58,  899.  ,  776.67,  788.79,  891.47,\n",
       "        828.73,  874.67,  796.43,  938.58,  588.55,  825.11,  828.27,\n",
       "        733.4 ,  754.26,  836.85,  963.76,  751.67, 1021.7 ,  843.18,\n",
       "        625.26,  970.49,  727.13,  762.51,  747.32,  608.57,  937.96,\n",
       "        912.46,  805.94,  790.89,  743.31,  702.5 ,  901.4 ,  971.55,\n",
       "        612.24,  780.07,  776.18,  804.51,  683.38,  994.11,  696.87,\n",
       "        680.51,  830.42,  829.45,  721.85,  602.71,  845.69,  798.54,\n",
       "        757.17,  800.49,  890.49,  850.44,  834.43,  573.53,  689.92,\n",
       "        704.06,  692.2 ,  993.06,  948.83,  826.91,  700.09,  763.54,\n",
       "        977.49,  772.38, 1034.7 ,  857.21,  773.6 ,  941.68,  780.37,\n",
       "        782.98,  751.48,  832.19,  830.27,  889.23,  830.64,  746.92,\n",
       "        804.48,  682.43,  558.6 ,  781.41,  959.49,  816.38,  860.01,\n",
       "        702.16, 1115.3 ,  801.58,  867.54,  837.86,  792.1 , 1011.1 ,\n",
       "        959.82,  829.68,  825.94,  699.84,  875.86,  835.  ,  889.76,\n",
       "        746.42,  769.34,  686.17,  638.89,  968.98,  746.87,  951.02,\n",
       "...\n",
       "       1088.9 ,  953.25,  975.47,  739.3 ,  683.59,  762.3 ,  829.64,\n",
       "        678.08, 1132.1 ,  927.19,  850.24,  920.17,  871.7 ,  923.98,\n",
       "        724.22,  634.18,  778.44,  829.65,  653.52, 1084.7 ,  866.22,\n",
       "       1104.4 ,  747.36,  802.2 ,  655.31,  745.85,  775.23,  777.17,\n",
       "        974.98,  861.16,  542.66,  924.5 ,  583.76,  944.95,  708.19,\n",
       "        711.34,  904.8 ,  832.63,  736.37,  755.84,  932.63,  877.49,\n",
       "        835.05,  894.63,  738.95,  729.29,  728.25,  727.56,  856.33,\n",
       "       1170.2 ,  677.33,  769.68,  917.17,  729.99,  906.88,  787.11,\n",
       "        758.53,  891.55, 1056.2 ,  765.5 ,  935.82,  654.82, 1060.4 ,\n",
       "        904.44,  754.59,  877.16,  705.33,  907.46, 1035.7 ,  778.12,\n",
       "        952.24,  987.31,  710.32,  813.24,  915.55,  832.4 ,  690.63,\n",
       "        924.76,  782.23, 1052.2 ,  915.47,  591.07,  580.45,  654.78,\n",
       "        951.81,  849.29,  752.82,  657.91,  645.62,  818.47,  925.21,\n",
       "        711.15,  977.86,  884.21,  814.93,  790.7 ,  832.93,  869.97,\n",
       "        772.97,  861.66,  971.8 ,  774.22,  717.59, 1068.1 ,  902.09,\n",
       "        779.6 ,  956.44,  890.78,  783.42,  863.74,  984.2 ,  689.48,\n",
       "        896.96,  838.66,  788.  ,  923.13,  940.1 ,  993.93,  711.93,\n",
       "        722.87,  805.08,  960.95,  762.37,  916.23,  758.47,  557.39,\n",
       "        982.54,  898.36,  834.27,  822.31,  984.25,  813.69,  751.59,\n",
       "       1047.6 ,  842.79,  799.76,  967.88])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ic_er</span></div><div class='xr-var-dims'>(ic_er_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>995.8 894.7 ... 1.123e+03 1.092e+03</div><input id='attrs-c5883a4d-c6b6-46ff-9138-d5404651ddde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5883a4d-c6b6-46ff-9138-d5404651ddde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad114652-0684-450b-96a5-604628c67a8e' class='xr-var-data-in' type='checkbox'><label for='data-ad114652-0684-450b-96a5-604628c67a8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 995.76,  894.69,  900.11,  831.04,  985.64,  862.56,  952.3 ,\n",
       "       1012.3 , 1024.8 ,  874.49,  774.69, 1325.  ,  880.8 ,  718.72,\n",
       "        736.77, 1092.4 ,  969.64,  853.82,  664.22, 1140.  ,  799.52,\n",
       "       1009.5 ,  880.97,  806.59, 1130.5 , 1187.9 ,  942.49,  913.32,\n",
       "        993.25,  941.41, 1144.4 ,  976.01, 1153.2 ,  963.54,  876.97,\n",
       "        897.8 ,  819.38, 1070.1 , 1060.2 , 1054.  ,  833.05, 1148.3 ,\n",
       "        938.83,  966.34, 1054.4 , 1098.1 ,  810.51, 1143.9 ,  929.43,\n",
       "       1012.7 ,  994.12, 1026.5 , 1278.3 ,  906.09, 1162.7 ,  884.68,\n",
       "        736.03, 1330.1 ,  878.61,  996.61, 1130.9 ,  810.97, 1100.5 ,\n",
       "        950.35, 1304.  , 1115.9 ,  856.94,  752.47, 1197.4 , 1043.9 ,\n",
       "        795.83,  946.68,  824.73,  831.37,  865.94, 1081.8 ,  916.09,\n",
       "       1191.8 , 1059.3 , 1159.6 ,  921.97,  790.37,  866.67,  948.05,\n",
       "        903.52, 1051.1 ,  923.99,  990.48,  845.59,  670.05,  964.66,\n",
       "       1131.4 ,  848.99, 1075.7 , 1031.8 ,  993.83,  856.06,  980.45,\n",
       "       1290.7 ,  862.07, 1185.4 , 1072.5 , 1023.6 ,  934.18,  902.04,\n",
       "       1039.1 ,  909.56,  961.74,  963.76, 1107.1 ,  946.4 ,  917.13,\n",
       "        822.85,  712.86,  714.01,  966.06, 1146.7 , 1120.  , 1048.7 ,\n",
       "        984.69, 1225.5 , 1125.4 , 1004.8 , 1027.6 , 1015.1 , 1142.5 ,\n",
       "        937.12,  879.88,  949.07,  875.49,  942.76,  957.79, 1028.2 ,\n",
       "       1143.9 ,  979.5 ,  931.27,  722.43,  942.23,  851.91, 1082.1 ,\n",
       "...\n",
       "       1210.  , 1168.1 , 1210.7 ,  989.9 ,  935.29,  824.84, 1015.9 ,\n",
       "       1121.  , 1174.5 , 1089.5 , 1116.4 , 1050.6 ,  910.56, 1069.6 ,\n",
       "        993.27,  777.28, 1150.1 , 1038.7 ,  911.5 , 1275.4 ,  906.27,\n",
       "       1416.7 ,  845.54, 1003.1 ,  891.1 ,  919.38,  854.6 ,  825.66,\n",
       "       1122.1 , 1022.7 ,  835.91, 1084.8 ,  658.38, 1147.2 ,  757.77,\n",
       "        794.42,  933.16,  965.96,  893.67,  912.42, 1123.3 , 1140.7 ,\n",
       "        894.26, 1173.7 ,  762.06,  859.23,  803.77,  825.  , 1131.5 ,\n",
       "       1417.8 ,  912.44,  766.79, 1034.7 ,  653.97, 1183.1 ,  865.21,\n",
       "        704.74, 1012.5 , 1263.4 , 1057.5 , 1023.5 ,  622.99, 1304.7 ,\n",
       "       1078.2 ,  972.28, 1050.2 ,  907.67,  907.25, 1017.8 ,  985.2 ,\n",
       "        891.76, 1157.5 ,  910.15,  809.95, 1105.8 ,  993.83,  890.9 ,\n",
       "       1175.6 ,  979.25, 1293.8 , 1026.3 ,  733.76,  777.04,  849.56,\n",
       "       1138.7 , 1255.9 ,  845.15,  843.31,  931.4 , 1029.8 , 1165.4 ,\n",
       "        871.97,  963.42, 1156.5 , 1150.3 , 1058.  , 1080.6 , 1080.4 ,\n",
       "       1042.4 ,  973.06,  943.57,  865.61,  863.93, 1328.  , 1057.7 ,\n",
       "        988.55, 1132.  ,  989.09,  685.23,  980.36, 1121.1 ,  840.17,\n",
       "        966.41, 1175.4 , 1117.6 , 1265.8 , 1148.6 , 1067.5 ,  972.16,\n",
       "       1169.7 ,  996.96,  965.23,  907.4 , 1067.7 , 1057.3 ,  650.59,\n",
       "       1042.7 ,  988.99,  987.05, 1045.3 , 1246.5 , 1064.1 ,  869.78,\n",
       "       1292.6 , 1041.3 , 1123.2 , 1091.7 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oc_ec</span></div><div class='xr-var-dims'>(oc_ec_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>664.0 624.3 526.9 ... 594.2 622.4</div><input id='attrs-6b1f84bc-492f-43bf-8e92-1509f1d8b45f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b1f84bc-492f-43bf-8e92-1509f1d8b45f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db2f5e8a-7c62-4261-b71c-4329c78b1cb9' class='xr-var-data-in' type='checkbox'><label for='data-db2f5e8a-7c62-4261-b71c-4329c78b1cb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([664.008, 624.288, 526.872, 579.6  , 485.292, 477.864, 574.032,\n",
       "       602.88 , 663.576, 638.664, 513.72 , 681.996, 488.052, 439.104,\n",
       "       509.652, 531.792, 519.096, 435.588, 409.392, 674.028, 529.188,\n",
       "       596.124, 500.784, 718.98 , 692.64 , 633.456, 584.388, 701.796,\n",
       "       532.584, 648.384, 623.124, 506.784, 720.696, 642.576, 503.52 ,\n",
       "       566.688, 537.24 , 591.828, 499.128, 589.32 , 632.616, 708.684,\n",
       "       518.904, 521.532, 546.096, 584.244, 463.596, 565.524, 656.28 ,\n",
       "       564.276, 501.384, 623.688, 671.916, 659.772, 647.748, 527.64 ,\n",
       "       499.392, 712.848, 474.24 , 524.52 , 550.26 , 473.448, 666.216,\n",
       "       585.408, 628.068, 578.172, 490.488, 473.796, 697.344, 586.788,\n",
       "       458.232, 579.78 , 484.992, 523.872, 439.368, 648.528, 468.072,\n",
       "       574.368, 648.804, 580.5  , 541.224, 518.736, 565.44 , 588.36 ,\n",
       "       557.472, 589.644, 530.724, 496.536, 504.312, 493.68 , 599.184,\n",
       "       589.488, 595.86 , 620.028, 728.496, 672.396, 522.744, 484.932,\n",
       "       667.8  , 632.736, 662.784, 472.596, 579.276, 604.608, 546.744,\n",
       "       583.536, 512.172, 470.628, 615.288, 682.608, 669.216, 528.156,\n",
       "       562.332, 424.152, 505.056, 587.712, 664.548, 613.416, 636.456,\n",
       "       654.984, 758.172, 578.304, 575.04 , 576.504, 560.592, 780.384,\n",
       "       490.992, 586.86 , 582.9  , 567.3  , 607.98 , 499.668, 536.76 ,\n",
       "       674.22 , 518.712, 598.872, 511.092, 561.876, 547.236, 557.58 ,\n",
       "...\n",
       "       729.12 , 691.02 , 647.256, 591.   , 585.54 , 486.444, 630.516,\n",
       "       576.912, 688.512, 652.884, 651.06 , 603.876, 644.4  , 616.896,\n",
       "       511.668, 427.152, 642.18 , 594.648, 456.348, 706.14 , 600.468,\n",
       "       796.368, 486.396, 613.932, 439.536, 506.604, 510.324, 414.024,\n",
       "       658.32 , 683.436, 380.7  , 517.08 , 423.552, 569.496, 441.888,\n",
       "       603.576, 580.38 , 668.256, 531.972, 580.44 , 475.8  , 644.988,\n",
       "       624.624, 664.44 , 399.588, 509.172, 434.292, 409.38 , 522.444,\n",
       "       814.404, 496.116, 589.176, 683.58 , 500.172, 597.876, 531.948,\n",
       "       449.4  , 660.204, 735.396, 658.956, 593.208, 386.94 , 661.584,\n",
       "       610.68 , 394.104, 590.472, 597.36 , 566.676, 609.06 , 569.16 ,\n",
       "       603.168, 614.148, 539.172, 477.228, 557.292, 513.18 , 479.64 ,\n",
       "       709.668, 596.532, 743.748, 572.244, 466.632, 406.632, 488.16 ,\n",
       "       632.676, 639.624, 494.016, 505.128, 514.92 , 539.1  , 647.304,\n",
       "       471.324, 634.2  , 614.928, 585.468, 626.712, 562.776, 677.52 ,\n",
       "       604.272, 658.152, 643.668, 474.516, 558.552, 773.664, 704.196,\n",
       "       508.932, 720.432, 539.436, 588.96 , 642.936, 613.992, 522.372,\n",
       "       603.24 , 596.772, 569.58 , 679.92 , 683.508, 602.784, 538.188,\n",
       "       630.816, 524.808, 648.192, 568.248, 531.756, 609.78 , 446.16 ,\n",
       "       598.536, 603.384, 481.284, 590.22 , 653.82 , 608.268, 569.376,\n",
       "       846.432, 574.632, 594.24 , 622.416])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oc_er</span></div><div class='xr-var-dims'>(oc_er_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>606.7 583.8 485.7 ... 481.9 550.2</div><input id='attrs-f0b3582d-c2ee-4516-9244-b85d465d1b60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0b3582d-c2ee-4516-9244-b85d465d1b60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8685c897-8cdb-4055-8eb0-3cee8436f954' class='xr-var-data-in' type='checkbox'><label for='data-8685c897-8cdb-4055-8eb0-3cee8436f954' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([606.72 , 583.788, 485.688, 510.264, 467.424, 445.248, 535.164,\n",
       "       557.724, 542.112, 566.868, 456.936, 570.864, 405.636, 474.168,\n",
       "       387.54 , 456.228, 535.656, 538.116, 348.888, 633.864, 343.548,\n",
       "       497.748, 434.688, 467.52 , 612.792, 546.96 , 512.532, 567.72 ,\n",
       "       528.252, 523.596, 596.028, 494.952, 660.264, 567.252, 438.192,\n",
       "       505.908, 410.832, 523.5  , 568.38 , 519.096, 480.552, 696.876,\n",
       "       479.748, 525.684, 491.664, 559.428, 355.692, 558.72 , 582.048,\n",
       "       613.944, 517.752, 446.76 , 679.86 , 421.752, 584.82 , 451.968,\n",
       "       439.788, 712.812, 479.844, 578.652, 409.14 , 489.132, 512.1  ,\n",
       "       558.   , 555.012, 505.428, 471.924, 331.92 , 613.68 , 533.052,\n",
       "       408.072, 489.732, 492.936, 560.292, 413.088, 494.928, 433.2  ,\n",
       "       438.852, 420.168, 503.172, 471.684, 353.136, 515.94 , 569.664,\n",
       "       509.472, 536.46 , 464.484, 477.768, 472.416, 460.572, 474.168,\n",
       "       553.368, 497.496, 548.724, 586.32 , 574.2  , 526.404, 476.676,\n",
       "       595.32 , 479.184, 634.272, 454.764, 546.144, 531.924, 479.784,\n",
       "       536.76 , 517.584, 475.296, 565.344, 587.472, 521.268, 439.812,\n",
       "       423.348, 284.004, 468.744, 376.212, 513.96 , 574.764, 574.32 ,\n",
       "       484.908, 702.132, 530.832, 535.032, 507.372, 479.064, 645.84 ,\n",
       "       483.972, 395.472, 499.956, 450.276, 516.864, 520.956, 516.468,\n",
       "       556.884, 501.432, 513.684, 377.7  , 557.376, 499.932, 614.1  ,\n",
       "...\n",
       "       636.432, 585.42 , 673.332, 477.252, 486.312, 374.568, 479.928,\n",
       "       534.396, 553.068, 605.112, 541.692, 519.756, 545.328, 544.38 ,\n",
       "       526.176, 438.552, 593.088, 598.932, 394.584, 797.316, 503.556,\n",
       "       685.86 , 449.736, 561.684, 422.76 , 477.42 , 528.276, 447.54 ,\n",
       "       683.868, 577.956, 402.012, 464.784, 424.884, 516.432, 389.16 ,\n",
       "       461.448, 444.012, 521.904, 427.008, 509.496, 449.28 , 589.416,\n",
       "       458.88 , 532.44 , 506.16 , 449.652, 400.548, 357.744, 576.   ,\n",
       "       730.716, 529.872, 478.98 , 603.684, 340.56 , 562.344, 542.436,\n",
       "       422.58 , 530.268, 651.444, 427.308, 650.796, 440.52 , 707.772,\n",
       "       561.96 , 406.44 , 497.232, 501.156, 480.876, 551.424, 563.46 ,\n",
       "       565.5  , 619.176, 517.68 , 548.172, 547.332, 502.956, 437.364,\n",
       "       598.656, 513.252, 638.868, 573.624, 369.432, 389.784, 398.76 ,\n",
       "       568.212, 606.876, 491.568, 377.436, 509.028, 475.704, 656.064,\n",
       "       400.752, 458.664, 673.512, 527.628, 446.244, 505.344, 549.672,\n",
       "       655.86 , 561.024, 587.856, 487.74 , 412.308, 645.456, 624.312,\n",
       "       436.968, 642.864, 572.004, 484.524, 481.344, 528.672, 440.82 ,\n",
       "       495.936, 529.992, 496.212, 666.48 , 442.62 , 605.712, 531.216,\n",
       "       414.684, 477.432, 506.34 , 556.272, 522.432, 556.464, 422.532,\n",
       "       512.808, 524.892, 403.812, 546.756, 587.34 , 491.112, 504.78 ,\n",
       "       661.932, 538.332, 481.872, 550.236])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41181b88-be48-4b6a-b149-b9d0d6e9a8a5' class='xr-section-summary-in' type='checkbox'  ><label for='section-41181b88-be48-4b6a-b149-b9d0d6e9a8a5' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ic_ec_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e86bc16-cc2b-431a-9fdd-b6f71bb0311b' class='xr-index-data-in' type='checkbox'/><label for='index-8e86bc16-cc2b-431a-9fdd-b6f71bb0311b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;ic_ec_dim_0&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ic_er_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51b22c2e-66c1-4d94-b992-6e2d278640a4' class='xr-index-data-in' type='checkbox'/><label for='index-51b22c2e-66c1-4d94-b992-6e2d278640a4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;ic_er_dim_0&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>oc_ec_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-688cdfd3-84cd-4bfd-87a6-d8ad4de5f8c8' class='xr-index-data-in' type='checkbox'/><label for='index-688cdfd3-84cd-4bfd-87a6-d8ad4de5f8c8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;oc_ec_dim_0&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>oc_er_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85084483-cc83-4ac0-b7f4-ed855307103c' class='xr-index-data-in' type='checkbox'/><label for='index-85084483-cc83-4ac0-b7f4-ed855307103c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;oc_er_dim_0&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-146b08fd-9953-4604-8209-2186dee75e15' class='xr-section-summary-in' type='checkbox'  checked><label for='section-146b08fd-9953-4604-8209-2186dee75e15' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-23T20:34:06.404039+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.15.1</dd></dl></div></li></ul></div></div><br></div>\n",
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      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> predictions\n",
       "\t> predictions_constant_data"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with model_3:\n",
    "    # update values of predictors with new 20% price increase in operating costs for electrical options\n",
    "    pm.set_data({\"oc_ec\": wide_heating_df[\"oc.ec\"] * 1.2, \"oc_er\": wide_heating_df[\"oc.er\"] * 1.2})\n",
    "    # use the updated values and predict outcomes and probabilities:\n",
    "    idata_new_policy = pm.sample_posterior_predictive(\n",
    "        idata_m3,\n",
    "        var_names=[\"p\", \"y_cat\"],\n",
    "        return_inferencedata=True,\n",
    "        predictions=True,\n",
    "        extend_inferencedata=False,\n",
    "        random_seed=100,\n",
    "    )\n",
    "\n",
    "idata_new_policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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       "array([0.05293203, 0.07065756, 0.66509458, 0.14841689, 0.06289894])\n",
       "Coordinates:\n",
       "  * alts_probs  (alts_probs) &lt;U2 40B &#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'p'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>alts_probs</span>: 5</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-21c61b01-a29e-4b30-ac40-01d8614973c9' class='xr-array-in' type='checkbox' checked><label for='section-21c61b01-a29e-4b30-ac40-01d8614973c9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.05293 0.07066 0.6651 0.1484 0.0629</span></div><div class='xr-array-data'><pre>array([0.05293203, 0.07065756, 0.66509458, 0.14841689, 0.06289894])</pre></div></div></li><li class='xr-section-item'><input id='section-0489732f-39ff-4482-9930-627c1b42bcd0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0489732f-39ff-4482-9930-627c1b42bcd0' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alts_probs</span></div><div class='xr-var-dims'>(alts_probs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</div><input id='attrs-8e3f7d1b-dd69-4c4d-81d8-29e42c047c19' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e3f7d1b-dd69-4c4d-81d8-29e42c047c19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5cb2c42-02be-4f46-94ad-4ec75bfbd34a' class='xr-var-data-in' type='checkbox'><label for='data-b5cb2c42-02be-4f46-94ad-4ec75bfbd34a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-43807b7d-a9db-4ff7-9c1b-2ea4be794c75' class='xr-section-summary-in' type='checkbox'  ><label for='section-43807b7d-a9db-4ff7-9c1b-2ea4be794c75' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>alts_probs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-99ad3d01-17ae-436b-8052-3e594bb1ab2e' class='xr-index-data-in' type='checkbox'/><label for='index-99ad3d01-17ae-436b-8052-3e594bb1ab2e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;alts_probs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-96f5390f-167f-4f0e-9132-a822e583cbfc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-96f5390f-167f-4f0e-9132-a822e583cbfc' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'p' (alts_probs: 5)> Size: 40B\n",
       "array([0.05293203, 0.07065756, 0.66509458, 0.14841689, 0.06289894])\n",
       "Coordinates:\n",
       "  * alts_probs  (alts_probs) <U2 40B 'ec' 'er' 'gc' 'gr' 'hp'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata_new_policy[\"predictions\"][\"p\"].mean(dim=[\"chain\", \"draw\", \"obs\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1011,
       "width": 2011
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(20, 10))\n",
    "counts = wide_heating_df.groupby(\"depvar\")[\"idcase\"].count()\n",
    "new_predictions = idata_new_policy[\"predictions\"][\"p\"].mean(dim=[\"chain\", \"draw\", \"obs\"]).values\n",
    "ci_lb = idata_m3[\"posterior\"][\"p\"].quantile(0.025, dim=[\"chain\", \"draw\", \"obs\"])\n",
    "ci_ub = idata_m3[\"posterior\"][\"p\"].quantile(0.975, dim=[\"chain\", \"draw\", \"obs\"])\n",
    "ax.scatter(ci_lb, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
    "ax.scatter(ci_ub, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
    "ax.scatter(\n",
    "    new_predictions,\n",
    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
    "    color=\"green\",\n",
    "    label=\"New Policy Predicted Share\",\n",
    "    s=100,\n",
    ")\n",
    "ax.scatter(\n",
    "    counts / counts.sum(),\n",
    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
    "    label=\"Observed Shares\",\n",
    "    color=\"red\",\n",
    "    s=100,\n",
    ")\n",
    "ax.hlines(\n",
    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
    "    ci_lb,\n",
    "    ci_ub,\n",
    "    label=\"Predicted 95% Credible Interval Old Policy\",\n",
    "    color=\"black\",\n",
    ")\n",
    "ax.set_title(\"Predicted Market Shares under Old and New Pricing Policy\", fontsize=20)\n",
    "ax.set_xlabel(\"Market Share\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gzheng/projects/professional/likert_dualresponse/.venv/lib/python3.11/site-packages/arviz/stats/stats.py:789: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>elpd_loo</th>\n",
       "      <th>p_loo</th>\n",
       "      <th>elpd_diff</th>\n",
       "      <th>weight</th>\n",
       "      <th>se</th>\n",
       "      <th>dse</th>\n",
       "      <th>warning</th>\n",
       "      <th>scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>m4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1020.574185</td>\n",
       "      <td>9.992352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.557224e-01</td>\n",
       "      <td>28.247545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m2</th>\n",
       "      <td>1</td>\n",
       "      <td>-1023.552608</td>\n",
       "      <td>5.038925</td>\n",
       "      <td>2.978423</td>\n",
       "      <td>2.305736e-01</td>\n",
       "      <td>27.799085</td>\n",
       "      <td>3.623620</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m3</th>\n",
       "      <td>2</td>\n",
       "      <td>-1025.780290</td>\n",
       "      <td>9.345972</td>\n",
       "      <td>5.206104</td>\n",
       "      <td>1.972889e-13</td>\n",
       "      <td>28.163863</td>\n",
       "      <td>3.007272</td>\n",
       "      <td>True</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m1</th>\n",
       "      <td>3</td>\n",
       "      <td>-1309.642978</td>\n",
       "      <td>1.228472</td>\n",
       "      <td>289.068792</td>\n",
       "      <td>1.370393e-02</td>\n",
       "      <td>12.918328</td>\n",
       "      <td>23.321276</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank     elpd_loo     p_loo   elpd_diff        weight         se  \\\n",
       "m4     0 -1020.574185  9.992352    0.000000  7.557224e-01  28.247545   \n",
       "m2     1 -1023.552608  5.038925    2.978423  2.305736e-01  27.799085   \n",
       "m3     2 -1025.780290  9.345972    5.206104  1.972889e-13  28.163863   \n",
       "m1     3 -1309.642978  1.228472  289.068792  1.370393e-02  12.918328   \n",
       "\n",
       "          dse  warning scale  \n",
       "m4   0.000000    False   log  \n",
       "m2   3.623620    False   log  \n",
       "m3   3.007272     True   log  \n",
       "m1  23.321276    False   log  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare = az.compare({\"m1\": idata_m1, \"m2\": idata_m2, \"m3\": idata_m3, \"m4\": idata_m4})\n",
    "compare"
   ]
  }
 ],
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