{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "domestic-remove",
   "metadata": {},
   "source": [
    "(bart_heteroscedasticity)=\n",
    "# Modeling Heteroscedasticity with BART\n",
    "\n",
    ":::{post} January, 2023\n",
    ":tags: BART, regression\n",
    ":category: beginner, reference\n",
    ":author: Juan Orduz\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72588976-efc3-4adc-bec2-bc5b6ac4b7e1",
   "metadata": {},
   "source": [
    "In this notebook we show how to use BART to model heteroscedasticity as described in Section 4.1 of [`pymc-bart`](https://github.com/pymc-devs/pymc-bart)'s paper {cite:p}`quiroga2022bart`. We use the `marketing` data set provided by the R package `datarium` {cite:p}`kassambara2019datarium`. The idea is to model a marketing channel contribution to sales as a function of budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "elect-softball",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import arviz.preview as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc as pm\n",
    "import pymc_bart as pmb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "level-balance",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = \"retina\"\n",
    "az.style.use(\"arviz-variat\")\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 6]\n",
    "rng = np.random.default_rng(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cae4407",
   "metadata": {},
   "source": [
    "## Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21e66b38",
   "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>youtube</th>\n",
       "      <th>facebook</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>276.12</td>\n",
       "      <td>45.36</td>\n",
       "      <td>83.04</td>\n",
       "      <td>26.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.40</td>\n",
       "      <td>47.16</td>\n",
       "      <td>54.12</td>\n",
       "      <td>12.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.64</td>\n",
       "      <td>55.08</td>\n",
       "      <td>83.16</td>\n",
       "      <td>11.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>181.80</td>\n",
       "      <td>49.56</td>\n",
       "      <td>70.20</td>\n",
       "      <td>22.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>216.96</td>\n",
       "      <td>12.96</td>\n",
       "      <td>70.08</td>\n",
       "      <td>15.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   youtube  facebook  newspaper  sales\n",
       "0   276.12     45.36      83.04  26.52\n",
       "1    53.40     47.16      54.12  12.48\n",
       "2    20.64     55.08      83.16  11.16\n",
       "3   181.80     49.56      70.20  22.20\n",
       "4   216.96     12.96      70.08  15.48"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    df = pd.read_csv(os.path.join(\"..\", \"data\", \"marketing.csv\"), sep=\";\", decimal=\",\")\n",
    "except FileNotFoundError:\n",
    "    df = pd.read_csv(pm.get_data(\"marketing.csv\"), sep=\";\", decimal=\",\")\n",
    "\n",
    "n_obs = df.shape[0]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d6364a9",
   "metadata": {},
   "source": [
    "## EDA\n",
    "\n",
    "We start by looking into the data. We are going to focus on *Youtube*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8bbae786",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1223,
       "width": 2023
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df[\"youtube\"], df[\"sales\"], \"o\", c=\"C0\")\n",
    "ax.set(title=\"Sales as a function of Youtube budget\", xlabel=\"budget\", ylabel=\"sales\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c3971f2",
   "metadata": {},
   "source": [
    "We clearly see that both the mean and variance are increasing as a function of budget. One possibility is to manually select an explicit parametrization of these functions, e.g. square root or logarithm. However, in this example we want to learn these functions from the data using a BART model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a3ce48",
   "metadata": {},
   "source": [
    "## Model Specification\n",
    "\n",
    "We proceed to prepare the data for modeling. We are going to use the `budget` as the predictor and `sales` as the response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db0bbf73",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[\"youtube\"].to_numpy().reshape(-1, 1)\n",
    "Y = df[\"sales\"].to_numpy()"
   ]
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    "Next, we specify the model. Note that we just need one BART distribution which can be vectorized to model both the mean and variance. We use a Gamma distribution as likelihood as we expect the sales to be positive."
   ]
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   "cell_type": "code",
   "execution_count": 6,
   "id": "6d85d918",
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    "with pm.Model() as model_marketing_full:\n",
    "    w = pmb.BART(\"w\", X=X, Y=np.log(Y), m=100, shape=(2, n_obs))\n",
    "    y = pm.Gamma(\"y\", mu=pm.math.exp(w[0]), sigma=pm.math.exp(w[1]), observed=Y)\n",
    "\n",
    "pm.model_to_graphviz(model=model_marketing_full)"
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   "id": "9aa885a3",
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   "source": [
    "We now fit the model."
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "id": "796874ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "PGBART: [w]\n"
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       "Output()"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 142 seconds.\n",
      "Sampling: [y]\n"
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       "Output()"
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    "with model_marketing_full:\n",
    "    idata_marketing_full = pm.sample(2000, random_seed=rng, compute_convergence_checks=False)\n",
    "    posterior_predictive_marketing_full = pm.sample_posterior_predictive(\n",
    "        trace=idata_marketing_full, random_seed=rng\n",
    "    )"
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   "source": [
    "## Results\n",
    "\n",
    "We can now visualize the posterior predictive distribution of the mean and the likelihood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04ca4edc",
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       "\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",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\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-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\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",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 13MB\n",
       "Dimensions:  (chain: 4, draw: 2000, y_dim_0: 200)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 32B 0 1 2 3\n",
       "  * draw     (draw) int64 16kB 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999\n",
       "  * y_dim_0  (y_dim_0) int64 2kB 0 1 2 3 4 5 6 7 ... 193 194 195 196 197 198 199\n",
       "Data variables:\n",
       "    y        (chain, draw, y_dim_0) float64 13MB 27.97 11.12 ... 28.21 21.44\n",
       "Attributes:\n",
       "    created_at:                 2025-12-03T06:39:41.407265+00:00\n",
       "    arviz_version:              0.23.0.dev0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.26.1</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-35a91811-0087-43eb-a6fe-97c8b5621c1c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-35a91811-0087-43eb-a6fe-97c8b5621c1c' 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>: 2000</li><li><span class='xr-has-index'>y_dim_0</span>: 200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ecb25b66-ba1e-4cc5-8a9f-0318ec618137' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ecb25b66-ba1e-4cc5-8a9f-0318ec618137' 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-e8e2be14-b32d-4bc6-be88-78421908718e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8e2be14-b32d-4bc6-be88-78421908718e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c34d298-3ef2-402b-b341-df08e51300de' class='xr-var-data-in' type='checkbox'><label for='data-5c34d298-3ef2-402b-b341-df08e51300de' 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 ... 1996 1997 1998 1999</div><input id='attrs-a575cc00-df30-40f6-9f2e-19cc4638e235' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a575cc00-df30-40f6-9f2e-19cc4638e235' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc52ddd0-ec3c-4b16-847e-1f6514bac37c' class='xr-var-data-in' type='checkbox'><label for='data-dc52ddd0-ec3c-4b16-847e-1f6514bac37c' 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, ..., 1997, 1998, 1999], shape=(2000,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_dim_0</span></div><div class='xr-var-dims'>(y_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-98f03f80-e014-49d9-8929-4858fe4d6436' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98f03f80-e014-49d9-8929-4858fe4d6436' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37c00c9e-dfb7-4821-85a2-c056703f9686' class='xr-var-data-in' type='checkbox'><label for='data-37c00c9e-dfb7-4821-85a2-c056703f9686' 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,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
       "       196, 197, 198, 199])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46d1e300-fa5b-4b3f-9241-3698819fcb06' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46d1e300-fa5b-4b3f-9241-3698819fcb06' 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</span></div><div class='xr-var-dims'>(chain, draw, y_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.97 11.12 8.761 ... 28.21 21.44</div><input id='attrs-ba60c4e6-fa70-46d7-a652-836686fccdb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba60c4e6-fa70-46d7-a652-836686fccdb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15063bfc-1ac0-4d8d-b62d-2f37671e028c' class='xr-var-data-in' type='checkbox'><label for='data-15063bfc-1ac0-4d8d-b62d-2f37671e028c' 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([[[27.96918581, 11.11815739,  8.76082305, ..., 19.19638054,\n",
       "         12.9498148 , 23.45546798],\n",
       "        [15.83238597, 11.54606299,  7.75893341, ..., 14.50547171,\n",
       "         16.91191259, 25.39039464],\n",
       "        [18.59913531,  9.65067566,  7.66837769, ..., 24.39690253,\n",
       "         15.65341258, 18.61704399],\n",
       "        ...,\n",
       "        [21.0635743 ,  9.71325338, 10.92949103, ..., 20.05816952,\n",
       "         24.312937  , 30.33811911],\n",
       "        [24.79295133, 10.79757347,  8.7496103 , ..., 18.92326227,\n",
       "         20.16220265, 23.73951782],\n",
       "        [17.66894215, 14.01971276,  7.28595655, ..., 20.55073429,\n",
       "         24.05316553, 24.93445267]],\n",
       "\n",
       "       [[16.81337939, 11.79211332,  7.2578813 , ..., 17.19528256,\n",
       "         16.59491285, 24.1055421 ],\n",
       "        [17.194388  , 13.16697397,  7.52538454, ..., 15.66281761,\n",
       "         21.24167974, 20.59267744],\n",
       "        [17.59791467, 12.9814301 ,  7.56312905, ..., 16.30644293,\n",
       "         34.22952095, 25.43403692],\n",
       "...\n",
       "        [35.39980663, 11.16892706,  4.85930448, ..., 17.50356059,\n",
       "         16.77209662, 25.90038116],\n",
       "        [21.22527455, 10.48682285,  7.40175012, ..., 22.81941626,\n",
       "         23.03904321, 27.56531224],\n",
       "        [17.77053379, 13.52420829,  6.27039596, ..., 19.10095456,\n",
       "         20.3477347 , 23.34570812]],\n",
       "\n",
       "       [[24.48114282, 11.40415354, 10.10443882, ..., 18.86606854,\n",
       "         18.60188911, 24.49011056],\n",
       "        [14.15488986,  9.83061879,  9.51900775, ...,  9.96946695,\n",
       "         23.26168605, 15.75838922],\n",
       "        [25.67715504, 11.12936249,  6.57787138, ..., 16.79871544,\n",
       "         24.70371024, 24.37506313],\n",
       "        ...,\n",
       "        [16.64876198, 12.68653474,  7.13648578, ..., 14.93170067,\n",
       "         17.05322727, 16.39640612],\n",
       "        [15.77879585, 11.31237833,  7.20675155, ..., 24.15944724,\n",
       "         26.83835268, 17.50811559],\n",
       "        [29.49958281, 11.83136434,  6.66171209, ..., 18.01028554,\n",
       "         28.2108319 , 21.43824353]]], shape=(4, 2000, 200))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c69e2d2-0818-4be8-9b54-5293ab2becee' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c69e2d2-0818-4be8-9b54-5293ab2becee' 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><input type='checkbox' disabled/><label></label><input id='index-f6e150ae-bdf5-4ad0-955e-e3062d77d29d' class='xr-index-data-in' type='checkbox'/><label for='index-f6e150ae-bdf5-4ad0-955e-e3062d77d29d' 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><input type='checkbox' disabled/><label></label><input id='index-5aa304eb-7708-45f9-a915-bed4ef8c5c2d' class='xr-index-data-in' type='checkbox'/><label for='index-5aa304eb-7708-45f9-a915-bed4ef8c5c2d' 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",
       "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d04face7-f128-4a1d-867e-43b48542cfd9' class='xr-index-data-in' type='checkbox'/><label for='index-d04face7-f128-4a1d-867e-43b48542cfd9' 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",
       "       190, 191, 192, 193, 194, 195, 196, 197, 198, 199],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;y_dim_0&#x27;, length=200))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8472bb1b-3214-470a-9bad-24ac9ba19002' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8472bb1b-3214-470a-9bad-24ac9ba19002' 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>2025-12-03T06:39:41.407265+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.23.0.dev0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.26.1</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_data342170d4-77ea-4112-a504-f707c253e8bf\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_data342170d4-77ea-4112-a504-f707c253e8bf\" 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",
       "<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(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, white)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
       "  );\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, #111111)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\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 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "  border: 2px solid transparent !important;\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:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0) !important;\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",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\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",
       "  border-color: var(--xr-background-color-row-even);\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",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\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-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\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",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3kB\n",
       "Dimensions:  (y_dim_0: 200)\n",
       "Coordinates:\n",
       "  * y_dim_0  (y_dim_0) int64 2kB 0 1 2 3 4 5 6 7 ... 193 194 195 196 197 198 199\n",
       "Data variables:\n",
       "    y        (y_dim_0) float64 2kB 26.52 12.48 11.16 22.2 ... 15.36 30.6 16.08\n",
       "Attributes:\n",
       "    created_at:                 2025-12-03T06:39:41.410923+00:00\n",
       "    arviz_version:              0.23.0.dev0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.26.1</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-d7c9372f-7100-4ba1-90d3-4f3c02778205' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d7c9372f-7100-4ba1-90d3-4f3c02778205' 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'>y_dim_0</span>: 200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2f119d44-0e94-4e22-accd-a894ba138381' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f119d44-0e94-4e22-accd-a894ba138381' 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'>y_dim_0</span></div><div class='xr-var-dims'>(y_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-c01d43cb-5874-4a55-9e8d-ac9579e184a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c01d43cb-5874-4a55-9e8d-ac9579e184a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aed74b6c-6bb4-4a44-aec4-dc866703eb8c' class='xr-var-data-in' type='checkbox'><label for='data-aed74b6c-6bb4-4a44-aec4-dc866703eb8c' 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,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
       "       196, 197, 198, 199])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b74ead9d-b717-4dab-adda-6736d4f5b228' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b74ead9d-b717-4dab-adda-6736d4f5b228' 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</span></div><div class='xr-var-dims'>(y_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>26.52 12.48 11.16 ... 30.6 16.08</div><input id='attrs-dba19734-c3bd-4fa9-9ac1-2991d81b61d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dba19734-c3bd-4fa9-9ac1-2991d81b61d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1910ee62-8b2a-45f6-9c20-a69a81c0d150' class='xr-var-data-in' type='checkbox'><label for='data-1910ee62-8b2a-45f6-9c20-a69a81c0d150' 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([26.52, 12.48, 11.16, 22.2 , 15.48,  8.64, 14.16, 15.84,  5.76,\n",
       "       12.72, 10.32, 20.88, 11.04, 11.64, 22.8 , 26.88, 15.  , 29.28,\n",
       "       13.56, 17.52, 21.6 , 15.  ,  6.72, 18.6 , 11.64, 14.4 , 18.  ,\n",
       "       19.08, 22.68, 12.6 , 25.68, 14.28, 11.52, 20.88, 11.4 , 15.36,\n",
       "       30.48, 17.64, 12.12, 25.8 , 19.92, 20.52, 24.84, 15.48, 10.2 ,\n",
       "       17.88, 12.72, 27.84, 17.76, 11.64, 13.68, 12.84, 27.12, 25.44,\n",
       "       24.24, 28.44,  6.6 , 15.84, 28.56, 22.08,  9.72, 29.04, 18.84,\n",
       "       16.8 , 21.6 , 11.16, 11.4 , 16.08, 22.68, 26.76, 21.96, 14.88,\n",
       "       10.56, 13.2 , 20.4 , 10.44,  8.28, 17.04,  6.36, 13.2 , 14.16,\n",
       "       14.76, 13.56, 16.32, 26.04, 18.24, 14.4 , 19.2 , 15.48, 20.04,\n",
       "       13.44,  8.76, 23.28, 26.64, 13.8 , 20.28, 14.04, 18.6 , 30.48,\n",
       "       20.64, 14.04, 28.56, 17.76, 17.64, 24.84, 23.04,  8.64, 10.44,\n",
       "        6.36, 23.76, 16.08, 26.16, 16.92, 19.08, 17.52, 15.12, 14.64,\n",
       "       11.28, 19.08,  7.92, 18.6 ,  8.4 , 13.92, 18.24, 23.64, 12.72,\n",
       "        7.92, 10.56, 29.64, 11.64,  1.92, 15.24,  6.84, 23.52, 12.96,\n",
       "       13.92, 11.4 , 24.96, 11.52, 24.84, 13.08, 23.04, 24.12, 12.48,\n",
       "       13.68, 12.36, 15.84, 30.48, 13.08, 12.12, 19.32, 13.92, 19.92,\n",
       "       22.8 , 18.72,  3.84, 18.36, 12.12,  8.76, 15.48, 17.28, 15.96,\n",
       "       17.88, 21.6 , 14.28, 14.28,  9.6 , 14.64, 20.52, 18.  , 10.08,\n",
       "       17.4 ,  9.12, 14.04, 13.8 , 32.4 , 24.24, 14.04, 14.16, 15.12,\n",
       "       12.6 , 14.64, 10.44, 31.44, 21.12, 27.12, 12.36, 20.76, 19.08,\n",
       "        8.04, 12.96, 11.88,  7.08, 23.52, 20.76,  9.12, 11.64, 15.36,\n",
       "       30.6 , 16.08])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-391f731e-4fcb-4556-8290-a6770ed597a1' class='xr-section-summary-in' type='checkbox'  ><label for='section-391f731e-4fcb-4556-8290-a6770ed597a1' 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>y_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3d10a07a-41ce-40e9-81de-8ce5264f9b11' class='xr-index-data-in' type='checkbox'/><label for='index-3d10a07a-41ce-40e9-81de-8ce5264f9b11' 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",
       "       190, 191, 192, 193, 194, 195, 196, 197, 198, 199],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;y_dim_0&#x27;, length=200))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-247dbe80-011d-4391-bf1e-b5ba3d79dd1b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-247dbe80-011d-4391-bf1e-b5ba3d79dd1b' 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>2025-12-03T06:39:41.410923+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.23.0.dev0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.26.1</dd></dl></div></li></ul></div></div><br></div>\n",
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      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior_predictive\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posterior_predictive_marketing_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2ffec14",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 2000x643.064 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 666,
       "width": 2023
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_plot = az.from_dict(\n",
    "    {\n",
    "        \"posterior_predictive\": {\n",
    "            \"y\": posterior_predictive_marketing_full.posterior_predictive[\"y\"]\n",
    "        },\n",
    "        \"observed_data\": {\"y\": df[\"sales\"].values},\n",
    "        \"constant_data\": {\"budget\": X[:, 0]},\n",
    "    },\n",
    "    dims={\n",
    "        \"y\": [\"budget_dim\"],\n",
    "        \"budget\": [\"budget_dim\"],\n",
    "    },\n",
    ")\n",
    "\n",
    "az.plot_lm(dt_plot, x=\"budget\", y=\"y\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1775acb",
   "metadata": {},
   "source": [
    "The fit looks good! In fact, we see that the mean and variance increase as a function of the budget."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b743d58b-2678-4e17-9947-a8fe4ed03e21",
   "metadata": {},
   "source": [
    "## Authors\n",
    "- Authored by [Juan Orduz](https://juanitorduz.github.io/) in Feb, 2023 \n",
    "- Rerun by Osvaldo Martin in Mar, 2023\n",
    "- Rerun by Osvaldo Martin in Nov, 2023\n",
    "- Rerun by Osvaldo Martin in Dec, 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "closed-frank",
   "metadata": {},
   "source": [
    "## References\n",
    ":::{bibliography}\n",
    ":filter: docname in docnames\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0717070c-04aa-4836-ab95-6b3eff0dcaaf",
   "metadata": {},
   "source": [
    "## Watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "sound-calculation",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Wed Dec 03 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.13.5\n",
      "IPython version      : 9.3.0\n",
      "\n",
      "pytensor: 2.35.1\n",
      "\n",
      "pymc      : 5.26.1\n",
      "numpy     : 2.2.6\n",
      "arviz     : 0.23.0.dev0\n",
      "matplotlib: 3.10.3\n",
      "pandas    : 2.3.0\n",
      "pymc_bart : 0.11.0\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e4386fc-4de9-4535-a160-d929315633ef",
   "metadata": {},
   "source": [
    ":::{include} ../page_footer.md\n",
    ":::"
   ]
  }
 ],
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