diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index da5d84d26..71974c5c2 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -82,7 +82,8 @@ repos:
                examples/samplers/SMC-ABC_Lotka-Volterra_example.ipynb|
                examples/splines/spline.ipynb|
                examples/survival_analysis/censored_data.ipynb|
-               examples/survival_analysis/weibull_aft.ipynb)
+               examples/survival_analysis/weibull_aft.ipynb|
+               examples/howto/custom_distribution.ipynb)
       entry: >
           (?x)(arviz-devs.github.io|
                aesara.readthedocs.io|
@@ -94,7 +95,9 @@ repos:
                docs.python.org|
                xarray.pydata.org
                python.arviz.org|
-               docs.xarray.dev)
+               docs.xarray.dev|
+               www.pymc.io|
+               docs.scipy.org/doc)
       language: pygrep
       types_or: [markdown, rst, jupyter]
 - repo: https://github.com/mwouts/jupytext
diff --git a/examples/diagnostics_and_criticism/model_averaging.ipynb b/examples/diagnostics_and_criticism/model_averaging.ipynb
index 86b5661e8..7b871c0ab 100644
--- a/examples/diagnostics_and_criticism/model_averaging.ipynb
+++ b/examples/diagnostics_and_criticism/model_averaging.ipynb
@@ -1,5 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(model_averaging)=\n",
+    "# Model Averaging\n",
+    "\n",
+    ":::{post} Aug 2022\n",
+    ":tags: model comparison, model averaging\n",
+    ":category: intermediate\n",
+    ":author: Osvaldo Martin\n",
+    ":::"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -18,7 +32,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on PyMC3 v3.11.0\n"
+      "Running on PyMC3 v3.11.5\n"
      ]
     }
    ],
@@ -65,17 +79,16 @@
     "tags": []
    },
    "source": [
-    "# Model averaging\n",
-    "\n",
-    "When confronted with more than one model we have several options. One of them is to perform model selection, using for example a given Information Criterion as exemplified [in this notebook](model_comparison.ipynb) and this other [example](GLM-model-selection.ipynb). Model selection is appealing for its simplicity, but we are discarding information about the uncertainty in our models. This is somehow similar to computing the full posterior and then just keep a point-estimate like the posterior mean; we may become overconfident of what we really know.\n",
+    "When confronted with more than one model we have several options. One of them is to perform model selection, using for example a given Information Criterion as exemplified the PyMC examples {ref}`pymc:model_comparison` and the {ref}`GLM-model-selection`. Model selection is appealing for its simplicity, but we are discarding information about the uncertainty in our models. This is somehow similar to computing the full posterior and then just keep a point-estimate like the posterior mean; we may become overconfident of what we really know. You can also browse the {doc}`blog/tag/model-comparison` tag to find related posts. \n",
     "\n",
     "One alternative is to perform model selection but discuss all the different models together with the computed values of a given Information Criterion. It is important to put all these numbers and tests in the context of our problem so that we and our audience can have a better feeling of the possible limitations and shortcomings of our methods. If you are in the academic world you can use this approach to add elements to the discussion section of a paper, presentation, thesis, and so on.\n",
     "\n",
-    "Yet another approach is to perform model averaging. The idea now is to generate a meta-model (and meta-predictions) using a weighted average of the models. There are several ways to do this and PyMC3 includes 3 of them that we are going to briefly discuss, you will find a more thorough explanation in the work by [Yuling Yao et. al.](https://arxiv.org/abs/1704.02030)\n",
+    "Yet another approach is to perform model averaging. The idea now is to generate a meta-model (and meta-predictions) using a weighted average of the models. There are several ways to do this and PyMC includes 3 of them that we are going to briefly discuss, you will find a more thorough explanation in the work by {cite:t}`Yao_2018`. PyMC integrates with ArviZ for model comparison. \n",
+    "\n",
     "\n",
     "## Pseudo Bayesian model averaging\n",
     "\n",
-    "Bayesian models can be weighted by their marginal likelihood, this is known as Bayesian Model Averaging. While this is theoretically appealing, is problematic in practice: on the one hand the marginal likelihood is highly sensible to the specification of the prior, in a way that parameter estimation is not, and on the other computing the marginal likelihood is usually a challenging task. An alternative route is to use the values of WAIC (Widely Applicable Information Criterion) or LOO (pareto-smoothed importance sampling Leave-One-Out cross-validation), which we will call generically IC, to estimate weights. We can do this by using the following formula:\n",
+    "Bayesian models can be weighted by their marginal likelihood, this is known as Bayesian Model Averaging. While this is theoretically appealing, it is problematic in practice: on the one hand the marginal likelihood is highly sensible to the specification of the prior, in a way that parameter estimation is not, and on the other, computing the marginal likelihood is usually a challenging task. An alternative route is to use the values of WAIC (Widely Applicable Information Criterion) or LOO (pareto-smoothed importance sampling Leave-One-Out cross-validation), which we will call generically IC, to estimate weights. We can do this by using the following formula:\n",
     "\n",
     "$$w_i = \\frac {e^{ - \\frac{1}{2} dIC_i }} {\\sum_j^M e^{ - \\frac{1}{2} dIC_j }}$$\n",
     "\n",
@@ -89,7 +102,7 @@
     "\n",
     "## Stacking\n",
     "\n",
-    "The third approach implemented in PyMC3 is know as _stacking of predictive distributions_ and it has been recently [proposed](https://arxiv.org/abs/1704.02030). We want to combine several models in a metamodel in order to minimize the diverge between the meta-model and the _true_ generating model, when using a logarithmic scoring rule this is equivalently to:\n",
+    "The third approach implemented in PyMC is known as _stacking of predictive distributions_ by {cite:t}`Yao_2018`. We want to combine several models in a metamodel in order to minimize the divergence between the meta-model and the _true_ generating model, when using a logarithmic scoring rule this is equivalent to:\n",
     "\n",
     "$$\\max_{w} \\frac{1}{n} \\sum_{i=1}^{n}log\\sum_{k=1}^{K} w_k p(y_i|y_{-i}, M_k)$$\n",
     "\n",
@@ -99,11 +112,11 @@
     "\n",
     "## Weighted posterior predictive samples\n",
     "\n",
-    "Once we have computed the weights, using any of the above 3 methods,  we can use them to get a weighted posterior predictive samples. PyMC3 offers functions to perform these steps in a simple way, so let see them in action using an example.\n",
+    "Once we have computed the weights, using any of the above 3 methods,  we can use them to get a weighted posterior predictive samples. PyMC offers functions to perform these steps in a simple way, so let see them in action using an example.\n",
     "\n",
-    "The following example is taken from the superb book [Statistical Rethinking](http://xcelab.net/rm/statistical-rethinking/) by Richard McElreath. You will find more PyMC3 examples from this book in this [repository](https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3). We are going to explore a simplified version of it. Check the book for the whole example and a more thorough discussion of both, the biological motivation for this problem and a theoretical/practical discussion of using Information Criteria to compare, select and average models.\n",
+    "The following example is taken from the superb book {cite:t}`mcelreath2018statistical` by Richard McElreath. You will find more PyMC examples from this book in the repository [Statistical-Rethinking-with-Python-and-PyMC](https://github.com/pymc-devs/pymc-resources/tree/main/Rethinking_2). We are going to explore a simplified version of it. Check the book for the whole example and a more thorough discussion of both, the biological motivation for this problem and a theoretical/practical discussion of using Information Criteria to compare, select and average models.\n",
     "\n",
-    "Briefly, our problem is as follows: We want to explore the composition of milk across several primate species, it is hypothesized that females from species of primates with larger brains produce more _nutritious_ milk (loosely speaking this is done _in order to_ support the development of such big brains). This is an important question for evolutionary biologists and try to give and answer we will use 3 variables, two predictor variables: the proportion of neocortex compare to the total mass of the brain and the logarithm of the body mass of the mothers. And for predicted variable, the kilocalories per gram of milk. With these variables we are going to build 3 different linear models:\n",
+    "Briefly, our problem is as follows: We want to explore the composition of milk across several primate species, it is hypothesized that females from species of primates with larger brains produce more _nutritious_ milk (loosely speaking this is done _in order to_ support the development of such big brains). This is an important question for evolutionary biologists and try to give an answer we will use 3 variables, two predictor variables: the proportion of neocortex compare to the total mass of the brain and the logarithm of the body mass of the mothers. And for predicted variable, the kilocalories per gram of milk. With these variables we are going to build 3 different linear models:\n",
     " \n",
     "1. A model using only the neocortex variable\n",
     "2. A model using only the logarithm of the mass variable\n",
@@ -253,25 +266,37 @@
       "NUTS: [sigma, beta, alpha]\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "\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"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "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='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [6000/6000 00:06<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      100.00% [6000/6000 00:04<00:00 Sampling 2 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -286,7 +311,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 21 seconds.\n"
+      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 12 seconds.\n"
      ]
     }
    ],
@@ -342,25 +367,37 @@
       "NUTS: [sigma, beta, alpha]\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "\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"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "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='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [6000/6000 00:07<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      100.00% [6000/6000 00:04<00:00 Sampling 2 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -375,7 +412,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 14 seconds.\n"
+      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 11 seconds.\n",
+      "The acceptance probability does not match the target. It is 0.8826043398520717, but should be close to 0.8. Try to increase the number of tuning steps.\n"
      ]
     }
    ],
@@ -432,25 +470,37 @@
       "NUTS: [sigma, beta, alpha]\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "\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"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "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='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [6000/6000 00:07<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      100.00% [6000/6000 00:06<00:00 Sampling 2 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -465,7 +515,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 18 seconds.\n"
+      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 12 seconds.\n"
      ]
     }
    ],
@@ -514,12 +564,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
+       "<Figure size 720x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -541,7 +593,7 @@
     "tags": []
    },
    "source": [
-    "Another option is to plot several traces in a same plot is to use `densityplot`. This plot is somehow similar to a forestplot, but we get truncated KDE plots (by default 95% credible intervals) grouped by variable names together with a point estimate (by default the mean)."
+    "Another option is to plot several traces in a same plot is to use `plot_density`. This plot is somehow similar to a forestplot, but we get truncated KDE (kernel density estimation) plots (by default 95% credible intervals) grouped by variable names together with a point estimate (by default the mean)."
    ]
   },
   {
@@ -560,17 +612,42 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 1656x552 with 2 Axes>"
+       "Text(0.5, 1.0, '95% Credible Intervals: sigma')"
       ]
      },
+     "execution_count": 8,
      "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 993.6x331.2 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "az.plot_density(traces, var_names=[\"alpha\", \"sigma\"]);"
+    "ax = az.plot_density(\n",
+    "    traces,\n",
+    "    var_names=[\"alpha\", \"sigma\"],\n",
+    "    shade=0.1,\n",
+    "    data_labels=[\"Model 0 (neocortex)\", \"Model 1 (log_mass)\", \"Model 2 (neocortex+log_mass)\"],\n",
+    ")\n",
+    "\n",
+    "ax[0, 0].set_xlabel(\"Density\")\n",
+    "ax[0, 0].set_ylabel(\"\")\n",
+    "ax[0, 0].set_title(\"95% Credible Intervals: alpha\")\n",
+    "\n",
+    "ax[0, 1].set_xlabel(\"Density\")\n",
+    "ax[0, 1].set_ylabel(\"\")\n",
+    "ax[0, 1].set_title(\"95% Credible Intervals: sigma\")"
    ]
   },
   {
@@ -586,7 +663,7 @@
     "tags": []
    },
    "source": [
-    "Now that we have sampled the posterior for the 3 models, we are going to use WAIC (Widely applicable information criterion) to compare the 3 models. We can do this using the `compare` function included with PyMC3."
+    "Now that we have sampled the posterior for the 3 models, we are going to use WAIC (Widely applicable information criterion) to compare the 3 models. We can do this using the `compare` function included with ArviZ."
    ]
   },
   {
@@ -603,14 +680,6 @@
     "tags": []
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/CloudChaoszero/opt/anaconda3/envs/pymc3-dev-py38/lib/python3.8/site-packages/arviz/stats/stats.py:146: UserWarning: The default method used to estimate the weights for each model,has changed from BB-pseudo-BMA to stacking\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -647,11 +716,11 @@
        "    <tr>\n",
        "      <th>model_2</th>\n",
        "      <td>0</td>\n",
-       "      <td>8.340604</td>\n",
-       "      <td>3.232120</td>\n",
+       "      <td>8.365702</td>\n",
+       "      <td>3.159733</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000e+00</td>\n",
-       "      <td>2.555994</td>\n",
+       "      <td>2.523747</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>False</td>\n",
        "      <td>log</td>\n",
@@ -659,24 +728,24 @@
        "    <tr>\n",
        "      <th>model_1</th>\n",
        "      <td>1</td>\n",
-       "      <td>4.299528</td>\n",
-       "      <td>2.140485</td>\n",
-       "      <td>4.041076</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>2.052167</td>\n",
-       "      <td>1.746865</td>\n",
+       "      <td>4.485976</td>\n",
+       "      <td>2.017674</td>\n",
+       "      <td>3.879726</td>\n",
+       "      <td>5.162537e-15</td>\n",
+       "      <td>2.054953</td>\n",
+       "      <td>1.705681</td>\n",
        "      <td>False</td>\n",
        "      <td>log</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>model_0</th>\n",
        "      <td>2</td>\n",
-       "      <td>3.433029</td>\n",
-       "      <td>2.044963</td>\n",
-       "      <td>4.907575</td>\n",
-       "      <td>3.352874e-14</td>\n",
-       "      <td>1.566360</td>\n",
-       "      <td>2.481639</td>\n",
+       "      <td>3.419154</td>\n",
+       "      <td>2.078996</td>\n",
+       "      <td>4.946548</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.577262</td>\n",
+       "      <td>2.453546</td>\n",
        "      <td>False</td>\n",
        "      <td>log</td>\n",
        "    </tr>\n",
@@ -686,9 +755,9 @@
       ],
       "text/plain": [
        "         rank       loo     p_loo     d_loo        weight        se       dse  \\\n",
-       "model_2     0  8.340604  3.232120  0.000000  1.000000e+00  2.555994  0.000000   \n",
-       "model_1     1  4.299528  2.140485  4.041076  0.000000e+00  2.052167  1.746865   \n",
-       "model_0     2  3.433029  2.044963  4.907575  3.352874e-14  1.566360  2.481639   \n",
+       "model_2     0  8.365702  3.159733  0.000000  1.000000e+00  2.523747  0.000000   \n",
+       "model_1     1  4.485976  2.017674  3.879726  5.162537e-15  2.054953  1.705681   \n",
+       "model_0     2  3.419154  2.078996  4.946548  0.000000e+00  1.577262  2.453546   \n",
        "\n",
        "         warning loo_scale  \n",
        "model_2    False       log  \n",
@@ -720,11 +789,11 @@
     "tags": []
    },
    "source": [
-    "We can see that the best model is `model_2`, the one with both predictor variables. Notice the DataFrame is ordered from lowest to highest WAIC (_i.e_ from _better_ to _worst_ model). Check [this notebook](model_comparison.ipynb) for a more detailed discussing on model comparison.\n",
+    "We can see that the best model is `model_2`, the one with both predictor variables. Notice the DataFrame is ordered from lowest to highest WAIC (_i.e_ from _better_ to _worst_ model). Check the {ref}`pymc:model_comparison` for a more detailed discussion on model comparison.\n",
     "\n",
-    "We can also see that we get a column with the relative `weight` for each model (according to the first equation at the beginning of this notebook). This weights can be _vaguely_ interpreted as the probability that each model will make the correct predictions on future data. Of course this interpretation is conditional on the models used to compute the weights, if we add or remove models the weights will change. And also is dependent on the assumptions behind WAIC (or any other Information Criterion used). So try to do not overinterpret these `weights`. \n",
+    "We can also see that we get a column with the relative `weight` for each model (according to the first equation at the beginning of this notebook). This weights can be _vaguely_ interpreted as the probability that each model will make the correct predictions on future data. Of course this interpretation is conditional on the models used to compute the weights, if we add or remove models the weights will change. And also is dependent on the assumptions behind WAIC (or any other Information Criterion used). So try to not overinterpret these `weights`. \n",
     "\n",
-    "Now we are going to use copmuted `weights` to generate predictions based not on a single model but on the weighted set of models. This is one way to perform model averaging. Using PyMC3 we can call the `sample_posterior_predictive_w` function as follows:"
+    "Now we are going to use computed `weights` to generate predictions based not on a single model, but on the weighted set of models. This is one way to perform model averaging. Using PyMC we can call the `sample_posterior_predictive_w` function as follows:"
    ]
   },
   {
@@ -741,25 +810,37 @@
     "tags": []
    },
    "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\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"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "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='4000' class='' max='4000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [4000/4000 00:10<00:00]\n",
+       "      100.00% [4000/4000 00:06<00:00]\n",
        "    </div>\n",
        "    "
       ],
@@ -795,7 +876,7 @@
    "source": [
     "Notice that we are passing the weights ordered by their index. We are doing this because we pass `traces` and `models` ordered from model 0 to 2, but the computed weights are ordered from lowest to highest WAIC (or equivalently from larger to lowest weight). In summary, we must be sure that we are correctly pairing the weights and models.\n",
     "\n",
-    "We are also going to compute PPCs for the lowest-WAIC model"
+    "We are also going to compute PPCs for the lowest-WAIC model."
    ]
   },
   {
@@ -829,7 +910,7 @@
     "tags": []
    },
    "source": [
-    "A simple way to compare both kind of predictions is to plot their mean and hpd interval"
+    "A simple way to compare both kind of predictions is to plot their mean and hpd interval."
    ]
   },
   {
@@ -848,12 +929,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 720x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -894,7 +977,39 @@
     "\n",
     "There are other ways to average models such as, for example, explicitly building a meta-model that includes all the models we have. We then perform parameter inference while jumping between the models. One problem with this approach is that jumping between models could hamper the proper sampling of the posterior.\n",
     "\n",
-    "Besides averaging discrete models we can sometimes think of continuous versions of them. A toy example is to imagine that we have a coin and we want to estimated it's degree of bias, a number between 0 and 1 being 0.5 equal chance of head and tails. We could think of two separated models one with a prior biased towards heads and one towards tails. We could fit both separate models and then average them using, for example, IC-derived weights. An alternative, is to build a hierarchical model to estimate the prior distribution, instead of contemplating two discrete models we will be computing a continuous model that includes these the discrete ones as particular cases. Which approach is better? That depends on our concrete problem. Do we have good reasons to think about two discrete models, or is our problem better represented with a continuous bigger model?"
+    "Besides averaging discrete models we can sometimes think of continuous versions of them. A toy example is to imagine that we have a coin and we want to estimated its degree of bias, a number between 0 and 1 having a 0.5 equal chance of head and tails (fair coin). We could think of two separate models one with a prior biased towards heads and one towards tails. We could fit both separate models and then average them using, for example, IC-derived weights. An alternative, is to build a hierarchical model to estimate the prior distribution, instead of contemplating two discrete models we will be computing a continuous model that includes these the discrete ones as particular cases. Which approach is better? That depends on our concrete problem. Do we have good reasons to think about two discrete models, or is our problem better represented with a continuous bigger model?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Authors\n",
+    "\n",
+    "* Authored by Osvaldo Martin in June 2017 ([pymc#2273](https://github.com/pymc-devs/pymc/pull/2273))\n",
+    "* Updated by Osvaldo Martin in December 2017 ([pymc#2741](https://github.com/pymc-devs/pymc/pull/2741))\n",
+    "* Updated by Marco Gorelli in November 2020 ([pymc#4271](https://github.com/pymc-devs/pymc/pull/4271))\n",
+    "* Moved from pymc to pymc-examples repo in December 2020 ([pymc-examples#8](https://github.com/pymc-devs/pymc-examples/pull/8))\n",
+    "* Updated by Raul Maldonado in February 2021 ([pymc#25](https://github.com/pymc-devs/pymc-examples/pull/25))\n",
+    "* Updated Markdown and styling by @reshamas in August 2022, ([pymc-examples#414](https://github.com/pymc-devs/pymc-examples/pull/414))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    ":::{bibliography}\n",
+    ":filter: docname in docnames\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Watermark"
    ]
   },
   {
@@ -915,19 +1030,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Sun Feb 07 2021\n",
+      "Last updated: Sun Aug 21 2022\n",
       "\n",
       "Python implementation: CPython\n",
-      "Python version       : 3.8.6\n",
-      "IPython version      : 7.20.0\n",
+      "Python version       : 3.10.5\n",
+      "IPython version      : 8.4.0\n",
       "\n",
-      "pymc3     : 3.11.0\n",
-      "numpy     : 1.20.0\n",
-      "matplotlib: None\n",
-      "pandas    : 1.2.1\n",
-      "arviz     : 0.11.0\n",
+      "pandas    : 1.4.3\n",
+      "matplotlib: 3.5.2\n",
+      "numpy     : 1.22.1\n",
+      "arviz     : 0.12.1\n",
+      "pymc3     : 3.11.5\n",
       "\n",
-      "Watermark: 2.1.0\n",
+      "Watermark: 2.3.1\n",
       "\n"
      ]
     }
@@ -936,13 +1051,21 @@
     "%load_ext watermark\n",
     "%watermark -n -u -v -iv -w"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python PyMC3 (Dev)",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "pymc3-dev-py38"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -954,7 +1077,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/references.bib b/examples/references.bib
index 0e66a52aa..a92240e67 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -448,6 +448,18 @@ @book{wilkinson2005grammar
   issn          = {1431-8784},
   isbn          = {978-0-387-24544-7}
 }
+@article{Yao_2018,
+  doi           = {10.1214/17-ba1091},
+  url           = {https://doi.org/10.1214\%2F17-ba1091},
+  year          = 2018,
+  month         = {sep},
+  publisher     = {Institute of Mathematical Statistics},
+  volume        = {13},
+  number        = {3},
+  author        = {Yuling Yao and Aki Vehtari and Daniel Simpson and Andrew Gelman},
+  title         = {Using Stacking to Average Bayesian Predictive Distributions (with Discussion)},
+  journal       = {Bayesian Analysis}
+}
 @article{yuan2009bayesian,
   title         = {Bayesian mediation analysis.},
   author        = {Yuan, Ying and MacKinnon, David P},
diff --git a/myst_nbs/diagnostics_and_criticism/model_averaging.myst.md b/myst_nbs/diagnostics_and_criticism/model_averaging.myst.md
index 41b6c2337..54c4ce251 100644
--- a/myst_nbs/diagnostics_and_criticism/model_averaging.myst.md
+++ b/myst_nbs/diagnostics_and_criticism/model_averaging.myst.md
@@ -6,11 +6,20 @@ jupytext:
     format_version: 0.13
     jupytext_version: 1.13.7
 kernelspec:
-  display_name: Python PyMC3 (Dev)
+  display_name: Python 3 (ipykernel)
   language: python
-  name: pymc3-dev-py38
+  name: python3
 ---
 
+(model_averaging)=
+# Model Averaging
+
+:::{post} Aug 2022
+:tags: model comparison, model averaging
+:category: intermediate
+:author: Osvaldo Martin
+:::
+
 ```{code-cell} ipython3
 ---
 papermill:
@@ -27,7 +36,7 @@ import numpy as np
 import pandas as pd
 import pymc3 as pm
 
-print(f"Running on PyMC3 v{pm.__version__}")
+print(f"Running on PyMC3 v{pm.__version__}")
 ```
 
 ```{code-cell} ipython3
@@ -47,17 +56,16 @@ az.style.use("arviz-darkgrid")
 
 +++ {"papermill": {"duration": 0.068882, "end_time": "2020-11-29T12:13:08.020372", "exception": false, "start_time": "2020-11-29T12:13:07.951490", "status": "completed"}, "tags": []}
 
-# Model averaging
-
-When confronted with more than one model we have several options. One of them is to perform model selection, using for example a given Information Criterion as exemplified [in this notebook](model_comparison.ipynb) and this other [example](GLM-model-selection.ipynb). Model selection is appealing for its simplicity, but we are discarding information about the uncertainty in our models. This is somehow similar to computing the full posterior and then just keep a point-estimate like the posterior mean; we may become overconfident of what we really know.
+When confronted with more than one model we have several options. One of them is to perform model selection, using for example a given Information Criterion as exemplified the PyMC examples {ref}`pymc:model_comparison` and the {ref}`GLM-model-selection`. Model selection is appealing for its simplicity, but we are discarding information about the uncertainty in our models. This is somehow similar to computing the full posterior and then just keep a point-estimate like the posterior mean; we may become overconfident of what we really know. You can also browse the {doc}`blog/tag/model-comparison` tag to find related posts. 
 
 One alternative is to perform model selection but discuss all the different models together with the computed values of a given Information Criterion. It is important to put all these numbers and tests in the context of our problem so that we and our audience can have a better feeling of the possible limitations and shortcomings of our methods. If you are in the academic world you can use this approach to add elements to the discussion section of a paper, presentation, thesis, and so on.
 
-Yet another approach is to perform model averaging. The idea now is to generate a meta-model (and meta-predictions) using a weighted average of the models. There are several ways to do this and PyMC3 includes 3 of them that we are going to briefly discuss, you will find a more thorough explanation in the work by [Yuling Yao et. al.](https://arxiv.org/abs/1704.02030)
+Yet another approach is to perform model averaging. The idea now is to generate a meta-model (and meta-predictions) using a weighted average of the models. There are several ways to do this and PyMC includes 3 of them that we are going to briefly discuss, you will find a more thorough explanation in the work by {cite:t}`Yao_2018`. PyMC integrates with ArviZ for model comparison. 
+
 
 ## Pseudo Bayesian model averaging
 
-Bayesian models can be weighted by their marginal likelihood, this is known as Bayesian Model Averaging. While this is theoretically appealing, is problematic in practice: on the one hand the marginal likelihood is highly sensible to the specification of the prior, in a way that parameter estimation is not, and on the other computing the marginal likelihood is usually a challenging task. An alternative route is to use the values of WAIC (Widely Applicable Information Criterion) or LOO (pareto-smoothed importance sampling Leave-One-Out cross-validation), which we will call generically IC, to estimate weights. We can do this by using the following formula:
+Bayesian models can be weighted by their marginal likelihood, this is known as Bayesian Model Averaging. While this is theoretically appealing, it is problematic in practice: on the one hand the marginal likelihood is highly sensible to the specification of the prior, in a way that parameter estimation is not, and on the other, computing the marginal likelihood is usually a challenging task. An alternative route is to use the values of WAIC (Widely Applicable Information Criterion) or LOO (pareto-smoothed importance sampling Leave-One-Out cross-validation), which we will call generically IC, to estimate weights. We can do this by using the following formula:
 
 $$w_i = \frac {e^{ - \frac{1}{2} dIC_i }} {\sum_j^M e^{ - \frac{1}{2} dIC_j }}$$
 
@@ -71,7 +79,7 @@ The above formula for computing weights is a very nice and simple approach, but
 
 ## Stacking
 
-The third approach implemented in PyMC3 is know as _stacking of predictive distributions_ and it has been recently [proposed](https://arxiv.org/abs/1704.02030). We want to combine several models in a metamodel in order to minimize the diverge between the meta-model and the _true_ generating model, when using a logarithmic scoring rule this is equivalently to:
+The third approach implemented in PyMC is known as _stacking of predictive distributions_ by {cite:t}`Yao_2018`. We want to combine several models in a metamodel in order to minimize the divergence between the meta-model and the _true_ generating model, when using a logarithmic scoring rule this is equivalent to:
 
 $$\max_{w} \frac{1}{n} \sum_{i=1}^{n}log\sum_{k=1}^{K} w_k p(y_i|y_{-i}, M_k)$$
 
@@ -81,11 +89,11 @@ The quantity $p(y_i|y_{-i}, M_k)$ is the leave-one-out predictive distribution f
 
 ## Weighted posterior predictive samples
 
-Once we have computed the weights, using any of the above 3 methods,  we can use them to get a weighted posterior predictive samples. PyMC3 offers functions to perform these steps in a simple way, so let see them in action using an example.
+Once we have computed the weights, using any of the above 3 methods,  we can use them to get a weighted posterior predictive samples. PyMC offers functions to perform these steps in a simple way, so let see them in action using an example.
 
-The following example is taken from the superb book [Statistical Rethinking](http://xcelab.net/rm/statistical-rethinking/) by Richard McElreath. You will find more PyMC3 examples from this book in this [repository](https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3). We are going to explore a simplified version of it. Check the book for the whole example and a more thorough discussion of both, the biological motivation for this problem and a theoretical/practical discussion of using Information Criteria to compare, select and average models.
+The following example is taken from the superb book {cite:t}`mcelreath2018statistical` by Richard McElreath. You will find more PyMC examples from this book in the repository [Statistical-Rethinking-with-Python-and-PyMC](https://github.com/pymc-devs/pymc-resources/tree/main/Rethinking_2). We are going to explore a simplified version of it. Check the book for the whole example and a more thorough discussion of both, the biological motivation for this problem and a theoretical/practical discussion of using Information Criteria to compare, select and average models.
 
-Briefly, our problem is as follows: We want to explore the composition of milk across several primate species, it is hypothesized that females from species of primates with larger brains produce more _nutritious_ milk (loosely speaking this is done _in order to_ support the development of such big brains). This is an important question for evolutionary biologists and try to give and answer we will use 3 variables, two predictor variables: the proportion of neocortex compare to the total mass of the brain and the logarithm of the body mass of the mothers. And for predicted variable, the kilocalories per gram of milk. With these variables we are going to build 3 different linear models:
+Briefly, our problem is as follows: We want to explore the composition of milk across several primate species, it is hypothesized that females from species of primates with larger brains produce more _nutritious_ milk (loosely speaking this is done _in order to_ support the development of such big brains). This is an important question for evolutionary biologists and try to give an answer we will use 3 variables, two predictor variables: the proportion of neocortex compare to the total mass of the brain and the logarithm of the body mass of the mothers. And for predicted variable, the kilocalories per gram of milk. With these variables we are going to build 3 different linear models:
  
 1. A model using only the neocortex variable
 2. A model using only the logarithm of the mass variable
@@ -211,7 +219,7 @@ az.plot_forest(traces, figsize=(10, 5));
 
 +++ {"papermill": {"duration": 0.052958, "end_time": "2020-11-29T12:14:55.196722", "exception": false, "start_time": "2020-11-29T12:14:55.143764", "status": "completed"}, "tags": []}
 
-Another option is to plot several traces in a same plot is to use `densityplot`. This plot is somehow similar to a forestplot, but we get truncated KDE plots (by default 95% credible intervals) grouped by variable names together with a point estimate (by default the mean).
+Another option is to plot several traces in a same plot is to use `plot_density`. This plot is somehow similar to a forestplot, but we get truncated KDE (kernel density estimation) plots (by default 95% credible intervals) grouped by variable names together with a point estimate (by default the mean).
 
 ```{code-cell} ipython3
 ---
@@ -223,12 +231,25 @@ papermill:
   status: completed
 tags: []
 ---
-az.plot_density(traces, var_names=["alpha", "sigma"]);
+ax = az.plot_density(
+    traces,
+    var_names=["alpha", "sigma"],
+    shade=0.1,
+    data_labels=["Model 0 (neocortex)", "Model 1 (log_mass)", "Model 2 (neocortex+log_mass)"],
+)
+
+ax[0, 0].set_xlabel("Density")
+ax[0, 0].set_ylabel("")
+ax[0, 0].set_title("95% Credible Intervals: alpha")
+
+ax[0, 1].set_xlabel("Density")
+ax[0, 1].set_ylabel("")
+ax[0, 1].set_title("95% Credible Intervals: sigma")
 ```
 
 +++ {"papermill": {"duration": 0.055089, "end_time": "2020-11-29T12:14:57.977616", "exception": false, "start_time": "2020-11-29T12:14:57.922527", "status": "completed"}, "tags": []}
 
-Now that we have sampled the posterior for the 3 models, we are going to use WAIC (Widely applicable information criterion) to compare the 3 models. We can do this using the `compare` function included with PyMC3.
+Now that we have sampled the posterior for the 3 models, we are going to use WAIC (Widely applicable information criterion) to compare the 3 models. We can do this using the `compare` function included with ArviZ.
 
 ```{code-cell} ipython3
 ---
@@ -247,11 +268,11 @@ comp
 
 +++ {"papermill": {"duration": 0.056609, "end_time": "2020-11-29T12:14:58.387481", "exception": false, "start_time": "2020-11-29T12:14:58.330872", "status": "completed"}, "tags": []}
 
-We can see that the best model is `model_2`, the one with both predictor variables. Notice the DataFrame is ordered from lowest to highest WAIC (_i.e_ from _better_ to _worst_ model). Check [this notebook](model_comparison.ipynb) for a more detailed discussing on model comparison.
+We can see that the best model is `model_2`, the one with both predictor variables. Notice the DataFrame is ordered from lowest to highest WAIC (_i.e_ from _better_ to _worst_ model). Check the {ref}`pymc:model_comparison` for a more detailed discussion on model comparison.
 
-We can also see that we get a column with the relative `weight` for each model (according to the first equation at the beginning of this notebook). This weights can be _vaguely_ interpreted as the probability that each model will make the correct predictions on future data. Of course this interpretation is conditional on the models used to compute the weights, if we add or remove models the weights will change. And also is dependent on the assumptions behind WAIC (or any other Information Criterion used). So try to do not overinterpret these `weights`. 
+We can also see that we get a column with the relative `weight` for each model (according to the first equation at the beginning of this notebook). This weights can be _vaguely_ interpreted as the probability that each model will make the correct predictions on future data. Of course this interpretation is conditional on the models used to compute the weights, if we add or remove models the weights will change. And also is dependent on the assumptions behind WAIC (or any other Information Criterion used). So try to not overinterpret these `weights`. 
 
-Now we are going to use copmuted `weights` to generate predictions based not on a single model but on the weighted set of models. This is one way to perform model averaging. Using PyMC3 we can call the `sample_posterior_predictive_w` function as follows:
+Now we are going to use computed `weights` to generate predictions based not on a single model, but on the weighted set of models. This is one way to perform model averaging. Using PyMC we can call the `sample_posterior_predictive_w` function as follows:
 
 ```{code-cell} ipython3
 ---
@@ -275,7 +296,7 @@ ppc_w = pm.sample_posterior_predictive_w(
 
 Notice that we are passing the weights ordered by their index. We are doing this because we pass `traces` and `models` ordered from model 0 to 2, but the computed weights are ordered from lowest to highest WAIC (or equivalently from larger to lowest weight). In summary, we must be sure that we are correctly pairing the weights and models.
 
-We are also going to compute PPCs for the lowest-WAIC model
+We are also going to compute PPCs for the lowest-WAIC model.
 
 ```{code-cell} ipython3
 ---
@@ -292,7 +313,7 @@ ppc_2 = pm.sample_posterior_predictive(trace=trace_2, model=model_2, progressbar
 
 +++ {"papermill": {"duration": 0.058214, "end_time": "2020-11-29T12:15:55.404271", "exception": false, "start_time": "2020-11-29T12:15:55.346057", "status": "completed"}, "tags": []}
 
-A simple way to compare both kind of predictions is to plot their mean and hpd interval
+A simple way to compare both kind of predictions is to plot their mean and hpd interval.
 
 ```{code-cell} ipython3
 ---
@@ -329,7 +350,30 @@ As we can see the mean value is almost the same for both predictions but the unc
 
 There are other ways to average models such as, for example, explicitly building a meta-model that includes all the models we have. We then perform parameter inference while jumping between the models. One problem with this approach is that jumping between models could hamper the proper sampling of the posterior.
 
-Besides averaging discrete models we can sometimes think of continuous versions of them. A toy example is to imagine that we have a coin and we want to estimated it's degree of bias, a number between 0 and 1 being 0.5 equal chance of head and tails. We could think of two separated models one with a prior biased towards heads and one towards tails. We could fit both separate models and then average them using, for example, IC-derived weights. An alternative, is to build a hierarchical model to estimate the prior distribution, instead of contemplating two discrete models we will be computing a continuous model that includes these the discrete ones as particular cases. Which approach is better? That depends on our concrete problem. Do we have good reasons to think about two discrete models, or is our problem better represented with a continuous bigger model?
+Besides averaging discrete models we can sometimes think of continuous versions of them. A toy example is to imagine that we have a coin and we want to estimated its degree of bias, a number between 0 and 1 having a 0.5 equal chance of head and tails (fair coin). We could think of two separate models one with a prior biased towards heads and one towards tails. We could fit both separate models and then average them using, for example, IC-derived weights. An alternative, is to build a hierarchical model to estimate the prior distribution, instead of contemplating two discrete models we will be computing a continuous model that includes these the discrete ones as particular cases. Which approach is better? That depends on our concrete problem. Do we have good reasons to think about two discrete models, or is our problem better represented with a continuous bigger model?
+
++++
+
+## Authors
+
+* Authored by Osvaldo Martin in June 2017 ([pymc#2273](https://github.com/pymc-devs/pymc/pull/2273))
+* Updated by Osvaldo Martin in December 2017 ([pymc#2741](https://github.com/pymc-devs/pymc/pull/2741))
+* Updated by Marco Gorelli in November 2020 ([pymc#4271](https://github.com/pymc-devs/pymc/pull/4271))
+* Moved from pymc to pymc-examples repo in December 2020 ([pymc-examples#8](https://github.com/pymc-devs/pymc-examples/pull/8))
+* Updated by Raul Maldonado in February 2021 ([pymc#25](https://github.com/pymc-devs/pymc-examples/pull/25))
+* Updated Markdown and styling by @reshamas in August 2022, ([pymc-examples#414](https://github.com/pymc-devs/pymc-examples/pull/414))
+
++++
+
+## References
+
+:::{bibliography}
+:filter: docname in docnames
+:::
+
++++
+
+## Watermark
 
 ```{code-cell} ipython3
 ---
@@ -344,3 +388,6 @@ tags: []
 %load_ext watermark
 %watermark -n -u -v -iv -w
 ```
+
+:::{include} ../page_footer.md
+:::