diff --git a/examples/BART/BART_introduction.ipynb b/examples/BART/BART_introduction.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d68537ba",
+   "metadata": {},
+   "source": [
+    "(BART_introduction)=\n",
+    "# Bayesian Additive Regression Trees: Introduction\n",
+    ":::{post} Dec 21, 2021\n",
+    ":tags: BART, Bayesian additive regression trees, non-parametric, pymc3.BART, pymc3.HalfNormal, pymc3.Model, pymc3.Normal, pymc3.Poisson, regression\n",
+    ":category: intermediate, explanation\n",
+    ":author: Osvaldo Martin\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7c087cca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on PyMC3 v4.0.0b2\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pymc as pm\n",
+    "\n",
+    "print(f\"Running on PyMC3 v{pm.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "25cf7b45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RANDOM_SEED = 8457\n",
+    "rng = np.random.RandomState(RANDOM_SEED)\n",
+    "az.style.use(\"arviz-darkgrid\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "444df604",
+   "metadata": {},
+   "source": [
+    "## BART overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86f379df",
+   "metadata": {},
+   "source": [
+    "Bayesian additive regression trees (BART) is a non-parametric regression approach. If we have some covariates $X$ and we want to use them to model $Y$, a BART model (omitting the priors) can be represented as:\n",
+    "\n",
+    "$$Y = f(X) + \\epsilon$$\n",
+    "\n",
+    "where we use a sum of $m$ [regression trees](https://en.wikipedia.org/wiki/Decision_tree_learning) to model $f$, and $\\epsilon$ is some noise. In the most typical examples $\\epsilon$ is normally distributed, $\\mathcal{N}(0, \\sigma)$. So we can also write:\n",
+    "\n",
+    "$$Y \\sim \\mathcal{N}(\\mu=BART(X), \\sigma)$$\n",
+    "\n",
+    "In principle nothing restricts us to use a sum of trees to model other relationship. For example we may have:\n",
+    "\n",
+    "$$Y \\sim \\text{Poisson}(\\mu=BART(X))$$\n",
+    "\n",
+    "One of the reason BART is Bayesian is the use of priors over the regression trees. The priors are defined in such a way that they favor shallow trees with leaf values close to zero. A key idea is that a single BART-tree is not very good at fitting the data but when we sum many of these trees we get a good and flexible approximation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e239c2c0",
+   "metadata": {},
+   "source": [
+    "## Coal mining with BART\n",
+    "\n",
+    "To better understand BART in practice we are going to use the oldie but goldie coal mining disaster dataset. One of the classic examples in PyMC. Instead of thinking this problem as a switch-point model with two Poisson distribution, as in the original PyMC example. We are going to think this problem as a non-parametric regression with a Poisson response (this is usually discussed in terms of [Poisson processes](https://en.wikipedia.org/wiki/Poisson_point_process) or [Cox processes](https://en.wikipedia.org/wiki/Cox_process), but we are OK without going into those technicalities). For a similar example but with Gaussian processes see [1](https://github.com/aloctavodia/BAP/blob/master/code/Chp7/07_Gaussian%20process.ipynb) or [2](https://research.cs.aalto.fi/pml/software/gpstuff/demo_lgcp.shtml). Because our data is just a single column with dates, we need to do some pre-processing. We are going to discretize the data, just as if we were building a histogram. We are going to use the centers of the bins as the variable $X$ and the counts per bin as the variable $Y$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "85bdba1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    coal = np.loadtxt(Path(\"..\", \"data\", \"coal.csv\"))\n",
+    "except FileNotFoundError:\n",
+    "    coal = np.loadtxt(pm.get_data(\"coal.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5d1221b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# discretize data\n",
+    "years = int(coal.max() - coal.min())\n",
+    "bins = years // 4\n",
+    "hist, x_edges = np.histogram(coal, bins=bins)\n",
+    "# compute the location of the centers of the discretized data\n",
+    "x_centers = x_edges[:-1] + (x_edges[1] - x_edges[0]) / 2\n",
+    "# xdata needs to be 2D for BART\n",
+    "x_data = x_centers[:, None]\n",
+    "# express data as the rate number of disaster per year\n",
+    "y_data = hist / 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98e0da5c",
+   "metadata": {},
+   "source": [
+    "In PyMC a BART variable can be defined very similar to other random variables. One important difference is that we have to pass ours Xs and Ys to the BART variable. Here we are also making explicit that we are going to use a sum over 20 trees (`m=20`). Low number of trees like 20 could be good enough for simple models like this and could also work very good as a quick approximation for more complex models in particular during the iterative or explorative phase of modeling. In those cases once we have more certainty about the model we really like we can improve the approximation by increasing `m`, in the literature is common to find reports of good results with numbers like 50, 100 or 200."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6ac663f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "BART is experimental. Use with caution.\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "PGBART: [μ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:23<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 24 seconds.\n",
+      "The number of effective samples is smaller than 10% for some parameters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with pm.Model(rng_seeder=rng) as model_coal:\n",
+    "    μ = pm.BART(\"μ\", X=x_data, Y=y_data, m=20)\n",
+    "    y_pred = pm.Poisson(\"y_pred\", mu=pm.math.exp(μ), observed=y_data)\n",
+    "    idata_coal = pm.sample()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e967bf5",
+   "metadata": {},
+   "source": [
+    "The white line in the following plot shows the median rate of accidents. The darker orange band represent the HDI 50% and the lighter one the 94%. We can see a rapid decrease of coal accidents between 1880 and 1900. Feel free to compare these results with those in the original {ref}`pymc:pymc_overview` example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1c715dbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "rates = np.exp(idata_coal.posterior[\"μ\"])\n",
+    "rate_median = np.exp(idata_coal.posterior[\"μ\"].median(dim=[\"draw\", \"chain\"]))\n",
+    "ax.plot(x_centers, rate_median, \"w\", lw=3)\n",
+    "az.plot_hdi(x_centers, rates, smooth=False)\n",
+    "az.plot_hdi(x_centers, rates, hdi_prob=0.5, smooth=False, plot_kwargs={\"alpha\": 0})\n",
+    "ax.plot(coal, np.zeros_like(coal) - 0.5, \"k|\")\n",
+    "ax.set_xlabel(\"years\")\n",
+    "ax.set_ylabel(\"rate\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81f778de",
+   "metadata": {},
+   "source": [
+    "In the previous plot the white line is the median over 4000 posterior draws, and each one of those posterior draws is a sum over `m=20` trees. \n",
+    "\n",
+    "\n",
+    "The following figure shows two samples from the posterior of $\\mu$. We can see that these functions are not smooth. This is fine and is a direct consequence of using regression trees. Trees can be seen as a way to represent stepwise functions, and a sum of stepwise functions is just another stepwise function. Thus, when using BART we just need to know that we are assuming that a stepwise function is a good enough approximation for our problem. In practice this is often the case because we sum over many trees, usually values like 50, 100 or 200. Additionally, we often average over the posterior distribution. All this makes the \"steps smoother\", even when we never really have an smooth function as for example with Gaussian processes (splines). A nice theoretical result, tells us that in the limit of $m \\to \\infty$ the BART prior converges to a [nowheredifferentiable](https://en.wikipedia.org/wiki/Weierstrass_function) Gaussian process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0c982c16",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.step(x_data, np.exp(pm.bart.predict(idata_coal, rng, size=2).T));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8633b7b4",
+   "metadata": {},
+   "source": [
+    "To gain further intuition the next figures show 3 of the `m` trees. As we can see these are definitely not very good approximators by themselves. inspecting individuals trees is generally not necessary. We are just showing them here to generate intuition about BART."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "252054ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bart_trees = idata_coal.sample_stats.bart_trees\n",
+    "for i in [0, 1, 2]:\n",
+    "    plt.step(x_data[:, 0], bart_trees[0, 0, i].item().predict_output())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7d04e15",
+   "metadata": {},
+   "source": [
+    "## Biking with BART"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abf8c799-b89e-48dc-a1c9-8366325a530a",
+   "metadata": {},
+   "source": [
+    "To explore other features offered by BART in PyMC. We are now going to move on to a different example. In this example we have data about the  number of bikes rental in a city, and we have chosen four covariates; the hour of the day, the temperature, the humidity and whether is a workingday or a weekend. This dataset is a subset of the [bike_sharing_dataset](http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "099f4c0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    bikes = pd.read_csv(Path(\"..\", \"data\", \"bikes.csv\"))\n",
+    "except FileNotFoundError:\n",
+    "    bikes = pd.read_csv(pm.get_data(\"bikes.csv\"))\n",
+    "\n",
+    "X = bikes[[\"hour\", \"temperature\", \"humidity\", \"workingday\"]]\n",
+    "Y = bikes[\"count\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5f8410b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "BART is experimental. Use with caution.\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [σ]\n",
+      ">PGBART: [μ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:02<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 62 seconds.\n",
+      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with pm.Model(rng_seeder=rng) as model_bikes:\n",
+    "    σ = pm.HalfNormal(\"σ\", Y.std())\n",
+    "    μ = pm.BART(\"μ\", X, Y, m=50)\n",
+    "    y = pm.Normal(\"y\", μ, σ, observed=Y)\n",
+    "    idata_bikes = pm.sample()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5daefd3e",
+   "metadata": {},
+   "source": [
+    "### Partial dependence plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99fd44df",
+   "metadata": {},
+   "source": [
+    "To help us interpret the results of our model we are going to use partial dependence plot. This is a type of plot that shows the marginal effect that one covariate has on the predicted variable. That is, what is the effect that a covariate $X_i$ has of $Y$ while we average over all the other covariates ($X_j, \\forall j \\not = i$). This type of plot are not exclusive of BART. But they are often used in the BART literature. PyMC provides an utility function to make this plot from the inference data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9bb3955d-98d6-40b0-ab1c-10082459f72a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/u/32/martino5/unix/proyectos/00_BM/arviz/arviz/plots/hdiplot.py:157: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n",
+      "  hdi_data = hdi(y, hdi_prob=hdi_prob, circular=circular, multimodal=False, **hdi_kwargs)\n",
+      "/u/32/martino5/unix/proyectos/00_BM/arviz/arviz/plots/hdiplot.py:157: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n",
+      "  hdi_data = hdi(y, hdi_prob=hdi_prob, circular=circular, multimodal=False, **hdi_kwargs)\n",
+      "/u/32/martino5/unix/proyectos/00_BM/arviz/arviz/plots/hdiplot.py:157: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n",
+      "  hdi_data = hdi(y, hdi_prob=hdi_prob, circular=circular, multimodal=False, **hdi_kwargs)\n",
+      "/u/32/martino5/unix/proyectos/00_BM/pymc3/pymc/bart/utils.py:241: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n",
+      "  hdi = az.hdi(new_Y[i])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pm.bart.plot_dependence(idata_bikes, X=X, Y=Y, grid=(2, 2), var_discrete=[3]);\n",
+    "# plt.savefig(\"pdp_discrete.png\", bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a86b72b",
+   "metadata": {},
+   "source": [
+    "From this plot we can see the main effect of each covariate on the predicted value. This is very useful we can recover complex relationship beyond monotonic increasing or decreasing effects. For example for the `hour` covariate we can see two peaks around 8 and and 17 hs and a minimum at midnight.\n",
+    "\n",
+    "When interpreting partial dependence plots we should be careful about the assumptions in this plot. First we are assuming variables are independent. For example when computing the effect of `hour` we have to marginalize the effect of `temperature` and this means that to compute the partial dependence value at `hour=0` we are including all observed values of temperature, and this may include temperatures that are actually not observed at midnight, given that lower temperatures are more likely than higher ones. We are seeing only averages, so if for a covariate half the values are positively associated with predicted variable and the other half negatively associated. The partial dependence plot will be flat as their contributions will cancel each other out. This is a problem that can be solved by using instead individual conditional expectation plots `pm.bart.plot_dependence(idata_bikes, kind=\"ice\")`. Notice that all this assumptions are assumptions of the partial dependence plot, not of our model! In fact BART can easily accommodate interaction of variables Although the prior in BART regularizes high order interactions). For more on interpreting Machine Learning model you could check the \"Interpretable Machine Learning\" book {cite:p}`molnar2019`.\n",
+    "\n",
+    "Finally like with other regression method we should be careful that the effects we are seeing on individual variables are conditional on the inclusion of the other variables. So for example, while `humidity` seems to be mostly flat, meaning that this covariate has an small effect of the number of used bikes. This could be the case because `humidity` and `temperature` are correlated to some extend and once we include `temperature` in our model `humidity` does not provide too much information. Try for example fitting the model again but this time with `humidity` as the single covariate and then fitting the model again with `hour` as a single covariate. You should see that the result for this single-variate models will very similar to the previous figure for the `hour` covariate, but less similar for the `humidity` covariate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54e31c66",
+   "metadata": {},
+   "source": [
+    "### Variable importance\n",
+    "\n",
+    "As we saw in the previous section a partial dependence plot can visualize give us an idea of how much each covariable contributes to the predicted outcome. But BART itself leads to a simple heuristic to estimate variable importance. That is simple count how many times a variable is included in all the regression trees. The intuition is that if a variable is important they it should appears more often in the fitted trees that less important variables. While this heuristic seems to provide reasonable results in practice, there is not too much theory justifying this procedure, at least not yet.\n",
+    "\n",
+    "The following plot shows the relative importance in a scale from 0 to 1 (less to more importance) and the sum of the individual importance is 1. See that, at least in this case, the relative importance qualitative agrees with the partial dependence plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2253e445",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, ax = plt.subplots(1)\n",
+    "VI = (\n",
+    "    idata_bikes.sample_stats[\"variable_inclusion\"]\n",
+    "    .stack(samples=(\"chain\", \"draw\"))\n",
+    "    .mean(\"samples\")\n",
+    "    .values\n",
+    ")\n",
+    "ax.plot(VI / VI.sum(), \"o-\")\n",
+    "ax.set_xticks(range(4))\n",
+    "ax.set_xticklabels([\"hour\", \"temperature\", \"humidity\", \"workingday\"])\n",
+    "ax.set_ylabel(\"relative importance\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "219cde48-b626-4325-afc0-2a2881fc5683",
+   "metadata": {},
+   "source": [
+    "## Authors\n",
+    "* Authored by Osvaldo Martin in Dec, 2021 ([pymc-examples#259](https://github.com/pymc-devs/pymc-examples/pull/259))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c184bc8",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    ":::{bibliography}\n",
+    ":filter: docname in docnames\n",
+    "\n",
+    "martin2018bayesian\n",
+    "martin2021bayesian\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c557ed8",
+   "metadata": {},
+   "source": [
+    "## Watermark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "608086f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Tue Jan 18 2022\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.8.8\n",
+      "IPython version      : 7.27.0\n",
+      "\n",
+      "numpy     : 1.20.3\n",
+      "pymc      : 4.0.0b1\n",
+      "matplotlib: 3.4.2\n",
+      "pandas    : 1.2.5\n",
+      "arviz     : 0.11.4\n",
+      "\n",
+      "Watermark: 2.2.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -n -u -v -iv -w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b02674f-26ad-4c07-bf60-eb8edd1c520b",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/data/bikes.csv b/examples/data/bikes.csv
new file mode 100644
index 000000000..54c325a4e
--- /dev/null
+++ b/examples/data/bikes.csv
@@ -0,0 +1,349 @@
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diff --git a/examples/data/coal.csv b/examples/data/coal.csv
new file mode 100644
index 000000000..64c80a085
--- /dev/null
+++ b/examples/data/coal.csv
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diff --git a/examples/references.bib b/examples/references.bib
index eec75a115..76b4da8a2 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -217,6 +217,13 @@ @book{martin2018bayesian
   publisher={Packt Publishing Ltd}
 }
 
+@book{martin2021bayesian,
+  title={Bayesian Modeling and Computation in Python},
+  author={Martin, Osvaldo A and Kumar, Ravin and Lao, Junpeng},
+  year={2021},
+  publisher={Chapman and Hall/CRC},
+  doi={10.1201/9781003019169}
+}
 
 @book{mcelreath2018statistical,
   title={Statistical rethinking: A Bayesian course with examples in R and Stan},
@@ -245,6 +252,15 @@ @misc{mnih2013playing
   primaryClass={cs.LG}
 }
 
+@book{molnar2019,
+  title = {Interpretable Machine Learning},
+  author = {Christoph Molnar},
+  year = {2019},
+  subtitle = {A Guide for Making Black Box Models Explainable},
+  url={https://christophm.github.io/interpretable-ml-book/},
+  publisher={Christoph Molnar}
+}
+
 @article{nowlan1992simplifying,
   title={Simplifying Neural Networks By Soft Weight-Sharing},
   author={Nowlan, Steven J and Hinton, Geoffrey E},
diff --git a/examples/table_of_contents_examples.js b/examples/table_of_contents_examples.js
index 437d91f20..766a1a2fd 100644
--- a/examples/table_of_contents_examples.js
+++ b/examples/table_of_contents_examples.js
@@ -1,4 +1,5 @@
 Gallery.contents = {
+    "BART/BART_introduction":"BART",
     "case_studies/BEST": "Case Studies",
     "case_studies/LKJ": "Case Studies",
     "case_studies/stochastic_volatility": "Case Studies",