diff --git a/examples/diagnostics_and_criticism/model_averaging.ipynb b/examples/diagnostics_and_criticism/model_averaging.ipynb
index da806ef10..37d5db3f2 100644
--- a/examples/diagnostics_and_criticism/model_averaging.ipynb
+++ b/examples/diagnostics_and_criticism/model_averaging.ipynb
@@ -7,7 +7,7 @@
     "(model_averaging)=\n",
     "# Model Averaging\n",
     "\n",
-    ":::{post} Aug 2022\n",
+    ":::{post} Aug 2024\n",
     ":tags: model comparison, model averaging\n",
     ":category: intermediate\n",
     ":author: Osvaldo Martin\n",
@@ -32,11 +32,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on PyMC v5.9.2\n"
+      "Running on PyMC v5.16.2+24.g799c98f41\n"
      ]
     }
    ],
    "source": [
+    "import os\n",
+    "\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -61,8 +63,7 @@
    },
    "outputs": [],
    "source": [
-    "RANDOM_SEED = 8927\n",
-    "np.random.seed(RANDOM_SEED)\n",
+    "rng = np.random.seed(2741)\n",
     "az.style.use(\"arviz-darkgrid\")"
    ]
   },
@@ -79,54 +80,57 @@
     "tags": []
    },
    "source": [
-    "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 by 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 somewhat similar to computing the full posterior and then just keeping 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",
+    "When confronted with more than one model we have several options. One of them is to perform model selection as exemplified by the PyMC examples {ref}`pymc:model_comparison` and the {ref}`GLM-model-selection`, usually is a good idea to also include posterior predictive checks in order to decide which model to keep. Discarding all models except one is equivalent to affirm that, among the evaluated models, one is correct (under some criteria) with probability 1 and the rest are incorrect. In most cases this will be an overstatment that ignores the uncertainty we have in our models. This is somewhat similar to computing the full posterior and then just keeping 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 to consider 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",
+    "An alternative to this dilema is to perform model selection but to acknoledge the models we discared. If the number of models are not that large this can be part of a technical discussion on a paper, presentation, thesis, and so on. If the audience is not technical enough, this may not be a good idea.\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. PyMC includes three methods that will be briefly discussed in this notebook. You will find a more thorough explanation in the work by {cite:t}`Yao_2018`. PyMC integrates with ArviZ for model comparison. \n",
+    "Yet another alternative, the topic of this example, is to perform model averaging. The idea is to weight each model by its merit and generate predictions from each model, proportional to those weights. There are several ways to do this, including the three methods that will be briefly discussed in this notebook. You will find a more thorough explanation in the work by {cite:t}`Yao_2018` and {cite:t}`Yao_2022`. \n",
     "\n",
     "\n",
     "## Pseudo Bayesian model averaging\n",
     "\n",
-    "Bayesian models can be weighted by their marginal likelihood, which 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 sensitive 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, which 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 sensitive 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. Additionally, Bayesian model averaging is flawed in the $\\mathcal{M}$-open setting in which the true data-generating process is not one of the candidate models being fit {cite:t}`Yao_2018`. A more robust approach is to compute the  expected log pointwise predictive density (ELPD).\n",
+    "\n",
+    "$$\n",
+    "\\sum_i^N \\log \\int \\ p(y_i \\mid \\theta) \\; p(\\theta \\mid y) d\\theta\n",
+    "$$\n",
+    "\n",
+    "where $N$ is the number of data points, $y_i$ is the i-th data point, $\\theta$ are the parameters of the model, $p(y_i \\mid \\theta)$ is the likelihood of the i-th data point given the parameters, and $p(\\theta \\mid y)$ is the posterior distribution.\n",
+    "\n",
+    "Once we have computed the ELPD for each model we can compute weights by doing\n",
     "\n",
-    "$$w_i = \\frac {e^{ - \\frac{1}{2} dIC_i }} {\\sum_j^M e^{ - \\frac{1}{2} dIC_j }}$$\n",
+    "$$w_i = \\frac {e^{dELPD_i}} {\\sum_j^M e^{dELPD_i}}$$\n",
     "\n",
-    "Where $dIC_i$ is the difference between the i-th information criterion value and the lowest one. Remember that the lower the value of the IC, the better. We can use any information criterion we want to compute a set of weights, but, of course, we cannot mix them. \n",
+    "Where $dELPD_i$ is the difference between the model with the best ELPD and the i-th model.\n",
     "\n",
-    "This approach is called pseudo Bayesian model averaging, or Akaike-like weighting and is an heuristic way to compute the relative probability of each model (given a fixed set of models) from the information criteria values. Note that the denominator is just a normalization term to ensure that the weights sum up to one.\n",
+    "This approach is called pseudo Bayesian model averaging, or Akaike-like weighting and is an heuristic to compute the relative probability of each model (given a fixed set of models). Note that we exponetiate to \"revert\" the effect of the logarithm in the ELPD formula and the denominator is a normalization term to ensure that the weights sum up to one. With a pinch of salt, we can interpret these weights as the probability of each model explaining the data.\n",
+    "\n",
+    "So far so good, but the ELPD is a theoretical quantity, and in practice we need to approximate it. To do so ArviZ offers two methods\n",
+    "\n",
+    "* WAIC, Widely Applicable Information Criterion\n",
+    "* LOO, Pareto-Smooth-Leave-One-Out-Cross-Validation.\n",
+    "\n",
+    "Both requiere and InferenceData with the log-likelihood group and are equally fast to compute. We recommend using LOO because it has better practical properties, and better diagnostics (so we known when we are having issues with the ELPD estimation).\n",
     "\n",
     "## Pseudo Bayesian model averaging with Bayesian Bootstrapping\n",
     "\n",
-    "The above formula for computing weights is a nice and simple approach, but with one major caveat: it does not take into account the uncertainty in the computation of the IC. We could compute the standard error of the IC (assuming a Gaussian approximation) and modify the above formula accordingly. Or we can do something more robust, like using a [Bayesian Bootstrapping](http://www.sumsar.net/blog/2015/04/the-non-parametric-bootstrap-as-a-bayesian-model/) to estimate, and incorporate this uncertainty.\n",
+    "The above formula for computing weights is a nice and simple approach, but with one major caveat: it does not take into account the uncertainty in the computation of the ELPD. We could compute the standard error of the ELPD value (assuming a Gaussian approximation) and modify the above formula accordingly. Or we can do something more robust, like using a [Bayesian Bootstrapping](http://www.sumsar.net/blog/2015/04/the-non-parametric-bootstrap-as-a-bayesian-model/) to estimate, and incorporate this uncertainty.\n",
     "\n",
     "## Stacking\n",
     "\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",
+    "The third approach we will discuss 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",
+    "$$\\max_{w} \\frac{1}{n} \\sum_{i=1}^{n}log\\sum_{k=1}^{K} w_k p(y_i \\mid y_{-i}, M_k)$$\n",
     "\n",
     "Where $n$ is the number of data points and $K$ the number of models. To enforce a solution we constrain $w$ to be $w_k \\ge 0$ and  $\\sum_{k=1}^{K} w_k = 1$. \n",
     "\n",
-    "The quantity $p(y_i|y_{-i}, M_k)$ is the leave-one-out predictive distribution for the $M_k$ model. Computing it requires fitting each model $n$ times, each time leaving out one data point. Fortunately we can approximate the exact leave-one-out predictive distribution using LOO (or even WAIC), and that is what we do in practice.\n",
+    "The quantity $p(y_i \\mid y_{-i}, M_k)$ is the leave-one-out predictive distribution for the $M_k$ model. Computing it requires fitting each model $n$ times, each time leaving out one data point. Fortunately, this is exactly what LOO approximates in a very efficient way. So we can use LOO and stacking together. To be fair, we can also use WAIC, even when WAIC approximates the ELPD in a different way.\n",
     "\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 weighted posterior predictive samples. PyMC offers functions to perform these steps in a simple way, so let's see them in action using an example.\n",
-    "\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",
+    "Once we have computed the weights, using any of the above 3 methods, we can use them to get weighted posterior predictive samples. We will illustrate how to do it using the body fat dataset {cite}`penrose1985`. This dataset has measurements from 251 individuals, including their weight, height, the circumference of the abdomen, the circumference of the wrist etc. Our purpose is to predict the percentage of body fat, as estimated by the siri variable, also available from the dataset.\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. To try to give an answer we will use 3 variables:\n",
-    "* two predictor variables - the proportion of neocortex mass compared to the total mass of the brain, and the logarithm of the body mass of the mothers. \n",
-    "* one predicted variable - the kilocalories per gram of milk. \n",
-    "\n",
-    "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",
-    "3. A model using both variables\n",
-    "\n",
-    "Let start by uploading the data and centering the `neocortex` and `log mass` variables, for better sampling."
+    "Let's start by loading the data"
    ]
   },
   {
@@ -164,53 +168,126 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>kcal.per.g</th>\n",
-       "      <th>neocortex</th>\n",
-       "      <th>log_mass</th>\n",
+       "      <th>siri</th>\n",
+       "      <th>age</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>height</th>\n",
+       "      <th>neck</th>\n",
+       "      <th>chest</th>\n",
+       "      <th>abdomen</th>\n",
+       "      <th>hip</th>\n",
+       "      <th>thigh</th>\n",
+       "      <th>knee</th>\n",
+       "      <th>ankle</th>\n",
+       "      <th>biceps</th>\n",
+       "      <th>forearm</th>\n",
+       "      <th>wrist</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.49</td>\n",
-       "      <td>-12.415882</td>\n",
-       "      <td>-0.831486</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>23</td>\n",
+       "      <td>70.1</td>\n",
+       "      <td>172</td>\n",
+       "      <td>36.2</td>\n",
+       "      <td>93.1</td>\n",
+       "      <td>85.2</td>\n",
+       "      <td>94.5</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>37.3</td>\n",
+       "      <td>21.9</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>27.4</td>\n",
+       "      <td>17.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.47</td>\n",
-       "      <td>-3.035882</td>\n",
-       "      <td>0.158913</td>\n",
+       "      <th>1</th>\n",
+       "      <td>6.1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>78.8</td>\n",
+       "      <td>184</td>\n",
+       "      <td>38.5</td>\n",
+       "      <td>93.6</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>98.7</td>\n",
+       "      <td>58.7</td>\n",
+       "      <td>37.3</td>\n",
+       "      <td>23.4</td>\n",
+       "      <td>30.5</td>\n",
+       "      <td>28.9</td>\n",
+       "      <td>18.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.56</td>\n",
-       "      <td>-3.035882</td>\n",
-       "      <td>0.181513</td>\n",
+       "      <th>2</th>\n",
+       "      <td>25.3</td>\n",
+       "      <td>22</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>168</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>95.8</td>\n",
+       "      <td>87.9</td>\n",
+       "      <td>99.2</td>\n",
+       "      <td>59.6</td>\n",
+       "      <td>38.9</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>28.8</td>\n",
+       "      <td>25.2</td>\n",
+       "      <td>16.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0.89</td>\n",
-       "      <td>0.064118</td>\n",
-       "      <td>-0.579032</td>\n",
+       "      <th>3</th>\n",
+       "      <td>10.4</td>\n",
+       "      <td>26</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>184</td>\n",
+       "      <td>37.4</td>\n",
+       "      <td>101.8</td>\n",
+       "      <td>86.4</td>\n",
+       "      <td>101.2</td>\n",
+       "      <td>60.1</td>\n",
+       "      <td>37.3</td>\n",
+       "      <td>22.8</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>29.4</td>\n",
+       "      <td>18.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.92</td>\n",
-       "      <td>1.274118</td>\n",
-       "      <td>-1.884978</td>\n",
+       "      <th>4</th>\n",
+       "      <td>28.7</td>\n",
+       "      <td>24</td>\n",
+       "      <td>83.8</td>\n",
+       "      <td>181</td>\n",
+       "      <td>34.4</td>\n",
+       "      <td>97.3</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>101.9</td>\n",
+       "      <td>63.2</td>\n",
+       "      <td>42.2</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>32.2</td>\n",
+       "      <td>27.7</td>\n",
+       "      <td>17.7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   kcal.per.g  neocortex  log_mass\n",
-       "0        0.49 -12.415882 -0.831486\n",
-       "5        0.47  -3.035882  0.158913\n",
-       "6        0.56  -3.035882  0.181513\n",
-       "7        0.89   0.064118 -0.579032\n",
-       "9        0.92   1.274118 -1.884978"
+       "   siri  age  weight  height  neck  chest  abdomen    hip  thigh  knee  ankle  \\\n",
+       "0  12.3   23    70.1     172  36.2   93.1     85.2   94.5   59.0  37.3   21.9   \n",
+       "1   6.1   22    78.8     184  38.5   93.6     83.0   98.7   58.7  37.3   23.4   \n",
+       "2  25.3   22    70.0     168  34.0   95.8     87.9   99.2   59.6  38.9   24.0   \n",
+       "3  10.4   26    84.0     184  37.4  101.8     86.4  101.2   60.1  37.3   22.8   \n",
+       "4  28.7   24    83.8     181  34.4   97.3    100.0  101.9   63.2  42.2   24.0   \n",
+       "\n",
+       "   biceps  forearm  wrist  \n",
+       "0    32.0     27.4   17.1  \n",
+       "1    30.5     28.9   18.2  \n",
+       "2    28.8     25.2   16.6  \n",
+       "3    32.4     29.4   18.2  \n",
+       "4    32.2     27.7   17.7  "
       ]
      },
      "execution_count": 3,
@@ -219,14 +296,11 @@
     }
    ],
    "source": [
-    "d = pd.read_csv(\n",
-    "    \"https://raw.githubusercontent.com/pymc-devs/resources/master/Rethinking_2/Data/milk.csv\",\n",
-    "    sep=\";\",\n",
-    ")\n",
-    "d = d[[\"kcal.per.g\", \"neocortex.perc\", \"mass\"]].rename({\"neocortex.perc\": \"neocortex\"}, axis=1)\n",
-    "d[\"log_mass\"] = np.log(d[\"mass\"])\n",
-    "d = d[~d.isna().any(axis=1)].drop(\"mass\", axis=1)\n",
-    "d.iloc[:, 1:] = d.iloc[:, 1:] - d.iloc[:, 1:].mean()\n",
+    "try:\n",
+    "    d = pd.read_csv(os.path.join(\"..\", \"data\", \"body_fat.csv\"))\n",
+    "except FileNotFoundError:\n",
+    "    d = pd.read_csv(pm.get_data(\"body_fat.csv\"))\n",
+    "\n",
     "d.head()"
    ]
   },
@@ -243,22 +317,13 @@
     "tags": []
    },
    "source": [
-    "Now that we have the data we are going to build our first model using only the `neocortex`."
+    "Now that we have the data we are going to build two models, both are simple linear regressions the difference is that for the first one we are going to use the variables `abdomen`, and for the second one we are going to use the variables `wrist`, `height` and `weight`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "papermill": {
-     "duration": 75.962348,
-     "end_time": "2020-11-29T12:14:25.303027",
-     "exception": false,
-     "start_time": "2020-11-29T12:13:09.340679",
-     "status": "completed"
-    },
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -272,79 +337,24 @@
     },
     {
      "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:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\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",
-       "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [12000/12000 00:00&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "534a7369174d40bcb342c3ee266992a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "Output()"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "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 1 seconds.\n",
-      "Sampling: [kcal]\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:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
+      "text/plain": []
      },
      "metadata": {},
      "output_type": "display_data"
@@ -352,149 +362,44 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:00&lt;00:00]\n",
-       "    </div>\n",
-       "    "
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
       ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with pm.Model() as model_0:\n",
-    "    alpha = pm.Normal(\"alpha\", mu=0, sigma=10)\n",
-    "    beta = pm.Normal(\"beta\", mu=0, sigma=10)\n",
-    "    sigma = pm.HalfNormal(\"sigma\", 10)\n",
-    "\n",
-    "    mu = alpha + beta * d[\"neocortex\"]\n",
-    "\n",
-    "    kcal = pm.Normal(\"kcal\", mu=mu, sigma=sigma, observed=d[\"kcal.per.g\"])\n",
-    "    trace_0 = pm.sample(2000, idata_kwargs={\"log_likelihood\": True})\n",
-    "    pm.sample_posterior_predictive(trace_0, extend_inferencedata=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.049578,
-     "end_time": "2020-11-29T12:14:25.401979",
-     "exception": false,
-     "start_time": "2020-11-29T12:14:25.352401",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "The second model is exactly the same as the first one, except we now use the logarithm of the mass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "papermill": {
-     "duration": 8.996265,
-     "end_time": "2020-11-29T12:14:34.447153",
-     "exception": false,
-     "start_time": "2020-11-29T12:14:25.450888",
-     "status": "completed"
     },
-    "tags": []
-   },
-   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [alpha, beta, sigma]\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 7 seconds.\n",
+      "Sampling: [siri]\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:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\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",
-       "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [12000/12000 00:00&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da2c4d9bc89345c2834b03d04dead94b",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "Output()"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "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 1 seconds.\n",
-      "Sampling: [kcal]\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:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
+      "text/plain": []
      },
      "metadata": {},
      "output_type": "display_data"
@@ -502,15 +407,11 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:00&lt;00:00]\n",
-       "    </div>\n",
-       "    "
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
       ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "\n"
       ]
      },
      "metadata": {},
@@ -518,48 +419,23 @@
     }
    ],
    "source": [
-    "with pm.Model() as model_1:\n",
-    "    alpha = pm.Normal(\"alpha\", mu=0, sigma=10)\n",
+    "with pm.Model() as model_0:\n",
+    "    alpha = pm.Normal(\"alpha\", mu=0, sigma=1)\n",
     "    beta = pm.Normal(\"beta\", mu=0, sigma=1)\n",
-    "    sigma = pm.HalfNormal(\"sigma\", 10)\n",
+    "    sigma = pm.HalfNormal(\"sigma\", 5)\n",
     "\n",
-    "    mu = alpha + beta * d[\"log_mass\"]\n",
+    "    mu = alpha + beta * d[\"abdomen\"]\n",
     "\n",
-    "    kcal = pm.Normal(\"kcal\", mu=mu, sigma=sigma, observed=d[\"kcal.per.g\"])\n",
+    "    siri = pm.Normal(\"siri\", mu=mu, sigma=sigma, observed=d[\"siri\"])\n",
     "\n",
-    "    trace_1 = pm.sample(2000, idata_kwargs={\"log_likelihood\": True})\n",
-    "    pm.sample_posterior_predictive(trace_1, extend_inferencedata=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.049839,
-     "end_time": "2020-11-29T12:14:34.547268",
-     "exception": false,
-     "start_time": "2020-11-29T12:14:34.497429",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "And finally the third model using the `neocortex` and `log_mass` variables"
+    "    idata_0 = pm.sample(idata_kwargs={\"log_likelihood\": True}, random_seed=rng)\n",
+    "    pm.sample_posterior_predictive(idata_0, extend_inferencedata=True, random_seed=rng)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "papermill": {
-     "duration": 19.373847,
-     "end_time": "2020-11-29T12:14:53.971081",
-     "exception": false,
-     "start_time": "2020-11-29T12:14:34.597234",
-     "status": "completed"
-    },
-    "tags": []
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -573,26 +449,13 @@
     },
     {
      "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:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "213552d5ddeb4dcb9b1758e941e77887",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "Output()"
       ]
      },
      "metadata": {},
@@ -601,15 +464,21 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [12000/12000 00:01&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
       ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "\n"
       ]
      },
      "metadata": {},
@@ -619,32 +488,19 @@
      "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 1 seconds.\n",
-      "Sampling: [kcal]\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 36 seconds.\n",
+      "Sampling: [siri]\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:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91f277f5f79f4e48a6f6b0df6d7b52df",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "Output()"
       ]
      },
      "metadata": {},
@@ -653,15 +509,21 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:00&lt;00:00]\n",
-       "    </div>\n",
-       "    "
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
       ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "\n"
       ]
      },
      "metadata": {},
@@ -669,322 +531,973 @@
     }
    ],
    "source": [
-    "with pm.Model() as model_2:\n",
-    "    alpha = pm.Normal(\"alpha\", mu=0, sigma=10)\n",
-    "    beta = pm.Normal(\"beta\", mu=0, sigma=1, shape=2)\n",
-    "    sigma = pm.HalfNormal(\"sigma\", 10)\n",
+    "with pm.Model() as model_1:\n",
+    "    alpha = pm.Normal(\"alpha\", mu=0, sigma=1)\n",
+    "    beta = pm.Normal(\"beta\", mu=0, sigma=1, shape=3)\n",
+    "    sigma = pm.HalfNormal(\"sigma\", 5)\n",
     "\n",
-    "    mu = alpha + pm.math.dot(beta, d[[\"neocortex\", \"log_mass\"]].T)\n",
+    "    mu = alpha + pm.math.dot(beta, d[[\"wrist\", \"height\", \"weight\"]].T)\n",
     "\n",
-    "    kcal = pm.Normal(\"kcal\", mu=mu, sigma=sigma, observed=d[\"kcal.per.g\"])\n",
+    "    siri = pm.Normal(\"siri\", mu=mu, sigma=sigma, observed=d[\"siri\"])\n",
     "\n",
-    "    trace_2 = pm.sample(2000, idata_kwargs={\"log_likelihood\": True})\n",
-    "    pm.sample_posterior_predictive(trace_2, extend_inferencedata=True)"
+    "    idata_1 = pm.sample(idata_kwargs={\"log_likelihood\": True}, random_seed=rng)\n",
+    "    pm.sample_posterior_predictive(idata_1, extend_inferencedata=True, random_seed=rng)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.050236,
-     "end_time": "2020-11-29T12:14:54.072799",
-     "exception": false,
-     "start_time": "2020-11-29T12:14:54.022563",
-     "status": "completed"
-    },
-    "tags": []
-   },
+   "metadata": {},
    "source": [
-    "Now that we have sampled the posterior for the 3 models, we are going to compare them visually. One option is to use the `forestplot` function that supports plotting more than one trace."
+    "Before LOO (or WAIC) to compare and or average models we should check that we do not have sampling issues and posterior predictive checks are resonable. For the sake of brevity we are going to skip these steps and instead jump to the model averaging.\n",
+    "\n",
+    "First we need to call `az.compare` to compute the LOO values for each model and the weights using `stacking`. These are the default options, if you want to perform pseudo Bayesian model averaging you can use the `method='BB-pseudo-BMA'` that includes the Bayesian Bootstrap estimation of the uncertainty in the ELPD.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "papermill": {
-     "duration": 0.967337,
-     "end_time": "2020-11-29T12:14:55.090748",
-     "exception": false,
-     "start_time": "2020-11-29T12:14:54.123411",
-     "status": "completed"
-    },
-    "tags": []
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rank</th>\n",
+       "      <th>elpd_loo</th>\n",
+       "      <th>p_loo</th>\n",
+       "      <th>elpd_diff</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>se</th>\n",
+       "      <th>dse</th>\n",
+       "      <th>warning</th>\n",
+       "      <th>scale</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>model_1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>-817.216895</td>\n",
+       "      <td>3.626704</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.639236</td>\n",
+       "      <td>10.496342</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>False</td>\n",
+       "      <td>log</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-825.344978</td>\n",
+       "      <td>1.832909</td>\n",
+       "      <td>8.128083</td>\n",
+       "      <td>0.360764</td>\n",
+       "      <td>9.970768</td>\n",
+       "      <td>8.698358</td>\n",
+       "      <td>False</td>\n",
+       "      <td>log</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
+       "         rank    elpd_loo     p_loo  elpd_diff    weight         se       dse  \\\n",
+       "model_1     0 -817.216895  3.626704   0.000000  0.639236  10.496342  0.000000   \n",
+       "model_0     1 -825.344978  1.832909   8.128083  0.360764   9.970768  8.698358   \n",
+       "\n",
+       "         warning scale  \n",
+       "model_1    False   log  \n",
+       "model_0    False   log  "
       ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "traces = [trace_0, trace_1, trace_2]\n",
-    "az.plot_forest(traces, figsize=(10, 5));"
+    "model_dict = dict(zip([\"model_0\", \"model_1\"], [idata_0, idata_1]))\n",
+    "comp = az.compare(model_dict)\n",
+    "comp"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "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": []
-   },
+   "metadata": {},
    "source": [
-    "Another option is to plot several traces in a same plot is to use `plot_density`. This plot is somewhat 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)."
+    "We can see from the column `weight`, that `model_1` has a weight of $\\approx 0.6$ and `model_2` has a weight $\\approx 0.4$. To use this weights to generate posterior predictive samples we can use the `az.weighted_posterior` function. This function takes the InferenceData objects and the weights and returns a new InferenceData object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "papermill": {
-     "duration": 2.61715,
-     "end_time": "2020-11-29T12:14:57.866426",
-     "exception": false,
-     "start_time": "2020-11-29T12:14:55.249276",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, '95% Credible Intervals: sigma')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1656x552 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "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\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "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": []
-   },
-   "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 ArviZ."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "papermill": {
-     "duration": 0.239084,
-     "end_time": "2020-11-29T12:14:58.272998",
-     "exception": false,
-     "start_time": "2020-11-29T12:14:58.033914",
-     "status": "completed"
-    },
-    "tags": []
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/adrienporter/anaconda3/envs/pymc-docs/lib/python3.11/site-packages/arviz/stats/stats.py:307: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'False' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
-      "  df_comp.loc[val] = (\n",
-      "/Users/adrienporter/anaconda3/envs/pymc-docs/lib/python3.11/site-packages/arviz/stats/stats.py:307: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'log' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
-      "  df_comp.loc[val] = (\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
        "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
+       "            <div>\n",
+       "              <div class='xr-header'>\n",
+       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
+       "              </div>\n",
+       "              <ul class=\"xr-sections group-sections\">\n",
+       "              \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_posterior_predictive3ac6144d-0c7f-42ec-82ef-bdde501263d0\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_posterior_predictive3ac6144d-0c7f-42ec-82ef-bdde501263d0\" class = \"xr-section-summary\">posterior_predictive</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:     (siri_dim_2: 251, sample: 3999)\n",
+       "Coordinates:\n",
+       "  * siri_dim_2  (siri_dim_2) int64 0 1 2 3 4 5 6 ... 244 245 246 247 248 249 250\n",
+       "  * sample      (sample) object MultiIndex\n",
+       "  * chain       (sample) int64 2 3 1 2 1 2 3 3 3 3 3 3 ... 3 3 3 1 0 0 2 0 2 1 2\n",
+       "  * draw        (sample) int64 682 691 550 397 831 520 ... 638 997 295 483 606 9\n",
+       "Data variables:\n",
+       "    siri        (siri_dim_2, sample) float64 17.75 16.43 14.7 ... 30.98 27.67\n",
+       "Attributes:\n",
+       "    created_at:                 2024-08-23T16:10:41.836182+00:00\n",
+       "    arviz_version:              0.20.0.dev0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.16.2+24.g799c98f41</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-30be2444-7de5-41b0-9df6-9f2cdc6e33b2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-30be2444-7de5-41b0-9df6-9f2cdc6e33b2' 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'>siri_dim_2</span>: 251</li><li><span class='xr-has-index'>sample</span>: 3999</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fba04221-594d-4639-8667-0ee8cfeaeb8d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fba04221-594d-4639-8667-0ee8cfeaeb8d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>siri_dim_2</span></div><div class='xr-var-dims'>(siri_dim_2)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 246 247 248 249 250</div><input id='attrs-88c62c32-deaf-4b37-8280-f71c51f7ee8c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88c62c32-deaf-4b37-8280-f71c51f7ee8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aebba92-9cbd-4c58-a86e-e48104577afa' class='xr-var-data-in' type='checkbox'><label for='data-0aebba92-9cbd-4c58-a86e-e48104577afa' 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, ..., 248, 249, 250])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-c1278449-a440-4619-aaa5-ee20d111a21a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1278449-a440-4619-aaa5-ee20d111a21a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2c8ecd4-935a-4a81-93e1-51bfee3bae1f' class='xr-var-data-in' type='checkbox'><label for='data-b2c8ecd4-935a-4a81-93e1-51bfee3bae1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(2, 682), (3, 691), (1, 550), ..., (2, 483), (1, 606), (2, 9)],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 1 2 1 2 3 3 ... 1 0 0 2 0 2 1 2</div><input id='attrs-a5743eb0-b5ee-4872-8df1-86e337d84d17' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a5743eb0-b5ee-4872-8df1-86e337d84d17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d36d8a35-d890-450a-8b00-1c45f030deda' class='xr-var-data-in' type='checkbox'><label for='data-d36d8a35-d890-450a-8b00-1c45f030deda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2, 3, 1, ..., 2, 1, 2])</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'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>682 691 550 397 ... 295 483 606 9</div><input id='attrs-64f0df5a-5c12-4fc3-83d2-5bedcf4c03d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-64f0df5a-5c12-4fc3-83d2-5bedcf4c03d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fa1c5d5-bf8f-41cb-836d-0dbc2c99fe18' class='xr-var-data-in' type='checkbox'><label for='data-3fa1c5d5-bf8f-41cb-836d-0dbc2c99fe18' 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([682, 691, 550, ..., 483, 606,   9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f00d1779-9c88-4d5b-b9eb-46da96959bb2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f00d1779-9c88-4d5b-b9eb-46da96959bb2' 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>siri</span></div><div class='xr-var-dims'>(siri_dim_2, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.75 16.43 14.7 ... 30.98 27.67</div><input id='attrs-796d0b2d-5998-4771-9352-2992733a73bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-796d0b2d-5998-4771-9352-2992733a73bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d92ba45-b59b-49c4-af7c-81f96cea970f' class='xr-var-data-in' type='checkbox'><label for='data-2d92ba45-b59b-49c4-af7c-81f96cea970f' 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([[17.7507863 , 16.43165785, 14.70207536, ..., 20.07166666,\n",
+       "         6.30707594, 23.71432073],\n",
+       "       [21.24130963, 13.56310432, 21.81044629, ..., 23.6387914 ,\n",
+       "         8.40753315, 29.83233935],\n",
+       "       [12.49832099,  9.77179084, 14.61533337, ..., 13.92368941,\n",
+       "         8.26722381, 27.29269372],\n",
+       "       ...,\n",
+       "       [27.34331012, 26.61542596, 17.51539238, ..., 28.37461601,\n",
+       "        26.33929509, 20.2096418 ],\n",
+       "       [23.22195584, 16.43333399, 32.8961469 , ..., 23.99824582,\n",
+       "        11.98555006, 19.34159072],\n",
+       "       [29.07441247, 26.16213246, 35.62032494, ..., 25.61299541,\n",
+       "        30.98457422, 27.66946849]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ddd7265-ef01-460f-9c16-3e4f50dbc25b' class='xr-section-summary-in' type='checkbox'  ><label for='section-9ddd7265-ef01-460f-9c16-3e4f50dbc25b' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>siri_dim_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-31003cab-0856-4130-b60d-1e18968445bd' class='xr-index-data-in' type='checkbox'/><label for='index-31003cab-0856-4130-b60d-1e18968445bd' 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",
+       "       241, 242, 243, 244, 245, 246, 247, 248, 249, 250],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;siri_dim_2&#x27;, length=251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sample<br>chain<br>draw</div></div><div class='xr-index-preview'>PandasMultiIndex</div><div></div><input id='index-e5296ff4-2d4d-46aa-9e6c-68d7f22be09f' class='xr-index-data-in' type='checkbox'/><label for='index-e5296ff4-2d4d-46aa-9e6c-68d7f22be09f' 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(MultiIndex([(2, 682),\n",
+       "            (3, 691),\n",
+       "            (1, 550),\n",
+       "            (2, 397),\n",
+       "            (1, 831),\n",
+       "            (2, 520),\n",
+       "            (3,  31),\n",
+       "            (3, 491),\n",
+       "            (3, 114),\n",
+       "            (3, 744),\n",
+       "            ...\n",
+       "            (3, 593),\n",
+       "            (3, 153),\n",
+       "            (1, 286),\n",
+       "            (0, 859),\n",
+       "            (0, 638),\n",
+       "            (2, 997),\n",
+       "            (0, 295),\n",
+       "            (2, 483),\n",
+       "            (1, 606),\n",
+       "            (2,   9)],\n",
+       "           name=&#x27;sample&#x27;, length=3999))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14de8ce8-a0fb-41cd-90be-f70dc66851c1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14de8ce8-a0fb-41cd-90be-f70dc66851c1' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-08-23T16:10:41.836182+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0.dev0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2+24.g799c98f41</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_dataa4d8b668-ecf9-4908-bae5-087361da27b3\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_observed_dataa4d8b668-ecf9-4908-bae5-087361da27b3\" 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(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
        "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>rank</th>\n",
-       "      <th>elpd_loo</th>\n",
-       "      <th>p_loo</th>\n",
-       "      <th>elpd_diff</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>se</th>\n",
-       "      <th>dse</th>\n",
-       "      <th>warning</th>\n",
-       "      <th>scale</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>model_2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>8.266521</td>\n",
-       "      <td>3.253300</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>2.557509</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>False</td>\n",
-       "      <td>log</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>model_1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4.340585</td>\n",
-       "      <td>2.122619</td>\n",
-       "      <td>3.925936</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>2.074807</td>\n",
-       "      <td>1.723294</td>\n",
-       "      <td>False</td>\n",
-       "      <td>log</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>model_0</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3.551017</td>\n",
-       "      <td>1.988888</td>\n",
-       "      <td>4.715504</td>\n",
-       "      <td>1.221245e-14</td>\n",
-       "      <td>1.587097</td>\n",
-       "      <td>2.493630</td>\n",
-       "      <td>False</td>\n",
-       "      <td>log</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         rank  elpd_loo     p_loo  elpd_diff        weight        se  \\\n",
-       "model_2     0  8.266521  3.253300   0.000000  1.000000e+00  2.557509   \n",
-       "model_1     1  4.340585  2.122619   3.925936  0.000000e+00  2.074807   \n",
-       "model_0     2  3.551017  1.988888   4.715504  1.221245e-14  1.587097   \n",
-       "\n",
-       "              dse  warning scale  \n",
-       "model_2  0.000000    False   log  \n",
-       "model_1  1.723294    False   log  \n",
-       "model_0  2.493630    False   log  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_dict = dict(zip([\"model_0\", \"model_1\", \"model_2\"], traces))\n",
-    "comp = az.compare(model_dict)\n",
-    "comp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "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": []
-   },
-   "source": [
-    "We can see that the best model is `model_2`, the one with both predictor variables. Note that the DataFrame is ordered from lowest to highest WAIC (_i.e_ from _best_ 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. It 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 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 ArviZ we can call the `az.stats.weight_predictions` function as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "papermill": {
-     "duration": 31.463179,
-     "end_time": "2020-11-29T12:15:29.907492",
-     "exception": false,
-     "start_time": "2020-11-29T12:14:58.444313",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<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",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:     (siri_dim_0: 251)\n",
+       "Coordinates:\n",
+       "  * siri_dim_0  (siri_dim_0) int64 0 1 2 3 4 5 6 ... 244 245 246 247 248 249 250\n",
+       "Data variables:\n",
+       "    siri        (siri_dim_0) float64 12.3 6.1 25.3 10.4 ... 33.6 29.3 26.0 31.9\n",
+       "Attributes:\n",
+       "    created_at:                 2024-08-23T16:10:41.440917+00:00\n",
+       "    arviz_version:              0.20.0.dev0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.16.2+24.g799c98f41</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-d6d43455-0774-4df2-b641-cf370813d760' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d6d43455-0774-4df2-b641-cf370813d760' 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'>siri_dim_0</span>: 251</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a972198b-3706-4b34-a5cc-2ca2d918d5f1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a972198b-3706-4b34-a5cc-2ca2d918d5f1' 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'>siri_dim_0</span></div><div class='xr-var-dims'>(siri_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 246 247 248 249 250</div><input id='attrs-7cb61985-3315-41a2-892b-ee63b6dc2850' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7cb61985-3315-41a2-892b-ee63b6dc2850' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36528e3f-02e3-4526-a8ce-8e5ef0e27b1a' class='xr-var-data-in' type='checkbox'><label for='data-36528e3f-02e3-4526-a8ce-8e5ef0e27b1a' 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, ..., 248, 249, 250])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ea2d9f77-76f4-47f2-857e-4a0d519aa2ed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ea2d9f77-76f4-47f2-857e-4a0d519aa2ed' 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>siri</span></div><div class='xr-var-dims'>(siri_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>12.3 6.1 25.3 ... 29.3 26.0 31.9</div><input id='attrs-cc104193-2ee8-4eed-a630-2d576825a054' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc104193-2ee8-4eed-a630-2d576825a054' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e1443da-b50a-42d0-a4e5-2f09334d6ac9' class='xr-var-data-in' type='checkbox'><label for='data-8e1443da-b50a-42d0-a4e5-2f09334d6ac9' 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([12.3,  6.1, 25.3, 10.4, 28.7, 20.9, 19.2, 12.4,  4.1, 11.7,  7.1,\n",
+       "        7.8, 20.8, 21.2, 22.1, 20.9, 29. , 22.9, 16. , 16.5, 19.1, 15.2,\n",
+       "       15.6, 17.7, 14. ,  3.7,  7.9, 22.9,  3.7,  8.8, 11.9,  5.7, 11.8,\n",
+       "       21.3, 32.3, 40.1, 24.2, 28.4, 32.6, 34.5, 32.9, 31.6, 32. ,  7.7,\n",
+       "       13.9, 10.8,  5.6, 13.6,  4. , 10.2,  6.6,  8. ,  6.3,  3.9, 22.6,\n",
+       "       20.4, 28. , 31.5, 24.6, 26.1, 29.8, 30.7, 25.8, 32.3, 30. , 21.5,\n",
+       "       13.8,  6.3, 12.9, 24.3,  8.8,  8.5, 13.5, 11.8, 18.5,  8.8, 22.2,\n",
+       "       21.5, 18.8, 31.4, 26.8, 18.4, 27. , 27. , 26.6, 14.9, 23.1,  8.3,\n",
+       "       14.1, 20.5, 18.2,  8.5, 24.9,  9. , 17.4,  9.6, 11.3, 17.8, 22.2,\n",
+       "       21.2, 20.4, 20.1, 22.3, 25.4, 18. , 19.3, 18.3, 17.3, 21.4, 19.7,\n",
+       "       28. , 22.1, 21.3, 26.7, 16.7, 20.1, 13.9, 25.8, 18.1, 27.9, 25.3,\n",
+       "       14.7, 16. , 13.8, 17.5, 27.2, 17.4, 20.8, 14.9, 18.1, 22.7, 23.6,\n",
+       "       26.1, 24.4, 27.1, 21.8, 29.4, 22.4, 20.4, 24.9, 18.3, 23.3,  9.4,\n",
+       "       10.3, 14.2, 19.2, 29.6,  5.3, 25.2,  9.4, 19.6, 10.1, 16.5, 21. ,\n",
+       "       17.3, 31.2, 10. , 12.5, 22.5,  9.4, 14.6, 13. , 15.1, 27.3, 19.2,\n",
+       "       21.8, 20.3, 34.3, 16.5,  3. ,  0.7, 20.5, 16.9, 25.3,  9.9, 13.1,\n",
+       "       29.9, 22.5, 16.9, 26.6,  0. , 11.5, 12.1, 17.5,  8.6, 23.6, 20.4,\n",
+       "       20.5, 24.4, 11.4, 38.1, 15.9, 24.7, 22.8, 25.5, 22. , 17.7,  6.6,\n",
+       "       23.6, 12.2, 22.1, 28.7,  6. , 34.8, 16.6, 32.9, 32.8,  9.6, 10.8,\n",
+       "        7.1, 27.2, 19.5, 18.7, 19.5, 47.5, 13.6,  7.5, 24.5, 15. , 12.4,\n",
+       "       26. , 11.5,  5.2, 10.9, 12.5, 14.8, 25.2, 14.9, 17. , 10.6, 16.1,\n",
+       "       15.4, 26.7, 25.8, 18.6, 24.8, 27.3, 12.4, 29.9, 17. , 35. , 30.4,\n",
+       "       32.6, 29. , 15.2, 30.2, 11. , 33.6, 29.3, 26. , 31.9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77366353-7dc0-45e1-ae7c-9321e90cdd33' class='xr-section-summary-in' type='checkbox'  ><label for='section-77366353-7dc0-45e1-ae7c-9321e90cdd33' 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>siri_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cbb6260e-b513-419d-bb08-0c08b43405a3' class='xr-index-data-in' type='checkbox'/><label for='index-cbb6260e-b513-419d-bb08-0c08b43405a3' 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",
+       "       241, 242, 243, 244, 245, 246, 247, 248, 249, 250],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;siri_dim_0&#x27;, length=251))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f5279a72-1a64-4bf7-af6b-7b6343a37d2f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5279a72-1a64-4bf7-af6b-7b6343a37d2f' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-08-23T16:10:41.440917+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0.dev0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2+24.g799c98f41</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "              </ul>\n",
+       "            </div>\n",
+       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
        " *\n",
        " */\n",
        "\n",
@@ -1000,7 +1513,6 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -1013,7 +1525,7 @@
        "}\n",
        "\n",
        ".xr-wrap {\n",
-       "  display: block !important;\n",
+       "  display: block;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
        "}\n",
@@ -1053,6 +1565,10 @@
        "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
        "}\n",
        "\n",
+       ".xr-sections.group-sections {\n",
+       "  grid-template-columns: auto;\n",
+       "}\n",
+       "\n",
        ".xr-section-item {\n",
        "  display: contents;\n",
        "}\n",
@@ -1230,11 +1746,6 @@
        "  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",
@@ -1256,16 +1767,14 @@
        "}\n",
        "\n",
        ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
+       ".xr-var-data {\n",
        "  display: none;\n",
        "  background-color: var(--xr-background-color) !important;\n",
        "  padding-bottom: 5px !important;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
        "  display: block;\n",
        "}\n",
        "\n",
@@ -1275,16 +1784,13 @@
        "\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",
+       ".xr-var-data {\n",
        "  grid-column: 1 / -1;\n",
        "}\n",
        "\n",
@@ -1322,8 +1828,7 @@
        "}\n",
        "\n",
        ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
+       ".xr-icon-file-text2 {\n",
        "  display: inline-block;\n",
        "  vertical-align: middle;\n",
        "  width: 1em;\n",
@@ -1332,152 +1837,42 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:     (kcal_dim_2: 17, sample: 7999)\n",
-       "Coordinates:\n",
-       "  * kcal_dim_2  (kcal_dim_2) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
-       "  * sample      (sample) object MultiIndex\n",
-       "  * chain       (sample) int64 0 2 0 0 0 0 0 1 3 1 2 3 ... 2 1 1 1 0 3 2 1 3 2 3\n",
-       "  * draw        (sample) int64 216 768 211 631 322 ... 1824 1709 95 1165 1267\n",
-       "Data variables:\n",
-       "    kcal        (kcal_dim_2, sample) float64 0.3795 0.3581 ... 0.3967 0.6766\n",
-       "Attributes:\n",
-       "    created_at:                 2023-11-20T05:39:30.790844\n",
-       "    arviz_version:              0.17.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.9.2</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-eec79787-db2b-4316-9818-3d45a0f952d0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eec79787-db2b-4316-9818-3d45a0f952d0' 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'>kcal_dim_2</span>: 17</li><li><span class='xr-has-index'>sample</span>: 7999</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d53b5cc5-f55c-4357-ab47-4050414e8adf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d53b5cc5-f55c-4357-ab47-4050414e8adf' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>kcal_dim_2</span></div><div class='xr-var-dims'>(kcal_dim_2)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 11 12 13 14 15 16</div><input id='attrs-a64bab8a-4f06-4910-9dc7-ec35c1e4f72c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a64bab8a-4f06-4910-9dc7-ec35c1e4f72c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2600aa05-db02-4e51-b9e5-b1d7613aab32' class='xr-var-data-in' type='checkbox'><label for='data-2600aa05-db02-4e51-b9e5-b1d7613aab32' 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, 14, 15, 16])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-05f1621d-7708-48ba-b1d9-1b7e484bc226' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05f1621d-7708-48ba-b1d9-1b7e484bc226' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7203b894-727e-4969-8b8a-c0cc54912d75' class='xr-var-data-in' type='checkbox'><label for='data-7203b894-727e-4969-8b8a-c0cc54912d75' 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, 216), (2, 768), (0, 211), ..., (3, 95), (2, 1165), (3, 1267)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 2 0 0 0 0 0 1 ... 1 0 3 2 1 3 2 3</div><input id='attrs-2c8543e5-7bdf-4085-bb2d-bd9a189ba41f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c8543e5-7bdf-4085-bb2d-bd9a189ba41f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3b331c2-b1d4-4344-b64c-f9e6083b95a8' class='xr-var-data-in' type='checkbox'><label for='data-b3b331c2-b1d4-4344-b64c-f9e6083b95a8' 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, 2, 0, ..., 3, 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'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>216 768 211 631 ... 95 1165 1267</div><input id='attrs-9fd0b008-e59e-4e98-9c75-b16f59d13d64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9fd0b008-e59e-4e98-9c75-b16f59d13d64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2cc4fa2-2f03-4655-a2cf-3a0c88258e29' class='xr-var-data-in' type='checkbox'><label for='data-e2cc4fa2-2f03-4655-a2cf-3a0c88258e29' 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([ 216,  768,  211, ...,   95, 1165, 1267])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4402d2d5-c773-42d9-a385-3c71a9b3c4f8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4402d2d5-c773-42d9-a385-3c71a9b3c4f8' 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>kcal</span></div><div class='xr-var-dims'>(kcal_dim_2, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3795 0.3581 ... 0.3967 0.6766</div><input id='attrs-a16c43c7-2bba-4dcc-91b1-7106fac0d1ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a16c43c7-2bba-4dcc-91b1-7106fac0d1ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-741f4ef4-3efc-431b-8efc-1b45be46ceeb' class='xr-var-data-in' type='checkbox'><label for='data-741f4ef4-3efc-431b-8efc-1b45be46ceeb' 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([[0.37948243, 0.35812455, 0.55341798, ..., 0.04554523, 0.49354075,\n",
-       "        0.30494875],\n",
-       "       [0.58812253, 0.71744306, 0.48415901, ..., 0.4476155 , 0.4318907 ,\n",
-       "        0.51617874],\n",
-       "       [0.45006656, 0.56388496, 0.67395765, ..., 0.52657786, 0.38888288,\n",
-       "        0.7367251 ],\n",
-       "       ...,\n",
-       "       [0.46592291, 0.64193949, 0.61722409, ..., 0.41269338, 0.37521453,\n",
-       "        0.31822142],\n",
-       "       [0.76051323, 0.59914   , 0.59858151, ..., 0.77325291, 0.70671105,\n",
-       "        0.83601586],\n",
-       "       [0.55466311, 0.66538659, 0.61705726, ..., 0.67386232, 0.39665465,\n",
-       "        0.67657363]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b344bd3e-7c71-4da4-90bf-8ec073558078' class='xr-section-summary-in' type='checkbox'  ><label for='section-b344bd3e-7c71-4da4-90bf-8ec073558078' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>kcal_dim_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-34817da9-1205-4971-bc5d-4c231c44b8ef' class='xr-index-data-in' type='checkbox'/><label for='index-34817da9-1205-4971-bc5d-4c231c44b8ef' 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, 10, 11, 12, 13, 14, 15, 16], dtype=&#x27;int64&#x27;, name=&#x27;kcal_dim_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sample<br>chain<br>draw</div></div><div class='xr-index-preview'>PandasMultiIndex</div><div></div><input id='index-ae552230-e9e2-472c-8500-5d6e2eb56fa7' class='xr-index-data-in' type='checkbox'/><label for='index-ae552230-e9e2-472c-8500-5d6e2eb56fa7' 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(MultiIndex([(0,  216),\n",
-       "            (2,  768),\n",
-       "            (0,  211),\n",
-       "            (0,  631),\n",
-       "            (0,  322),\n",
-       "            (0,  310),\n",
-       "            (0,  976),\n",
-       "            (1,   85),\n",
-       "            (3, 1549),\n",
-       "            (1, 1519),\n",
-       "            ...\n",
-       "            (1, 1736),\n",
-       "            (1, 1686),\n",
-       "            (1,  633),\n",
-       "            (0,  998),\n",
-       "            (3, 1520),\n",
-       "            (2, 1824),\n",
-       "            (1, 1709),\n",
-       "            (3,   95),\n",
-       "            (2, 1165),\n",
-       "            (3, 1267)],\n",
-       "           name=&#x27;sample&#x27;, length=7999))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7f2249d-a9bf-4fe4-acdb-176b3fa25677' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7f2249d-a9bf-4fe4-acdb-176b3fa25677' 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>2023-11-20T05:39:30.790844</dd><dt><span>arviz_version :</span></dt><dd>0.17.0.dev0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.9.2</dd></dl></div></li></ul></div></div>"
+       ".xr-wrap{width:700px!important;} </style>"
       ],
       "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:     (kcal_dim_2: 17, sample: 7999)\n",
-       "Coordinates:\n",
-       "  * kcal_dim_2  (kcal_dim_2) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
-       "  * sample      (sample) object MultiIndex\n",
-       "  * chain       (sample) int64 0 2 0 0 0 0 0 1 3 1 2 3 ... 2 1 1 1 0 3 2 1 3 2 3\n",
-       "  * draw        (sample) int64 216 768 211 631 322 ... 1824 1709 95 1165 1267\n",
-       "Data variables:\n",
-       "    kcal        (kcal_dim_2, sample) float64 0.3795 0.3581 ... 0.3967 0.6766\n",
-       "Attributes:\n",
-       "    created_at:                 2023-11-20T05:39:30.790844\n",
-       "    arviz_version:              0.17.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.9.2"
+       "Inference data with groups:\n",
+       "\t> posterior_predictive\n",
+       "\t> observed_data"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ppc_w = az.stats.weight_predictions(\n",
+    "ppc_w = az.weight_predictions(\n",
     "    [model_dict[name] for name in comp.index],\n",
     "    weights=comp.weight,\n",
-    ").posterior_predictive\n",
+    ")\n",
     "ppc_w"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.058454,
-     "end_time": "2020-11-29T12:15:30.024455",
-     "exception": false,
-     "start_time": "2020-11-29T12:15:29.966001",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "We are also going to name the PPC for the lowest-WAIC model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "papermill": {
-     "duration": 25.204481,
-     "end_time": "2020-11-29T12:15:55.287049",
-     "exception": false,
-     "start_time": "2020-11-29T12:15:30.082568",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "ppc_2 = trace_2.posterior_predictive"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "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": []
-   },
+   "metadata": {},
    "source": [
-    "A simple way to compare both kind of predictions is to plot their mean and hpd interval."
+    "From the following plot we can see that the avearged model is a combination of the two models."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "papermill": {
-     "duration": 0.301319,
-     "end_time": "2020-11-29T12:15:55.764128",
-     "exception": false,
-     "start_time": "2020-11-29T12:15:55.462809",
-     "status": "completed"
-    },
-    "tags": []
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -1487,43 +1882,34 @@
     }
    ],
    "source": [
-    "mean_w = ppc_w[\"kcal\"].mean()\n",
-    "hpd_w = az.hdi(ppc_w, var_names=\"kcal\", input_core_dims=[[\"sample\", \"kcal_dim_2\"]])\n",
-    "\n",
-    "mean = ppc_2[\"kcal\"].mean()\n",
-    "hpd = az.hdi(ppc_2, var_names=\"kcal\", input_core_dims=[[\"chain\", \"draw\", \"kcal_dim_2\"]])\n",
-    "\n",
-    "plt.plot(mean_w, 1, \"C0o\", label=\"weighted models\")\n",
-    "plt.hlines(1, *hpd_w[\"kcal\"], colors=\"C0\")\n",
-    "plt.plot(mean, 0, \"C1o\", label=\"model 2\")\n",
-    "plt.hlines(0, *hpd[\"kcal\"], \"C1\")\n",
-    "\n",
-    "plt.yticks([])\n",
-    "plt.ylim(-1, 2)\n",
-    "plt.xlabel(\"kcal per g\")\n",
-    "plt.legend();"
+    "az.plot_kde(\n",
+    "    idata_0.posterior_predictive[\"siri\"].values,\n",
+    "    plot_kwargs={\"color\": \"C0\", \"linestyle\": \"--\"},\n",
+    "    label=\"model_0\",\n",
+    ")\n",
+    "az.plot_kde(\n",
+    "    idata_1.posterior_predictive[\"siri\"].values,\n",
+    "    plot_kwargs={\"color\": \"C0\", \"linestyle\": \"--\"},\n",
+    "    label=\"model_1\",\n",
+    ")\n",
+    "az.plot_kde(\n",
+    "    ppc_w.posterior_predictive[\"siri\"].values,\n",
+    "    plot_kwargs={\"color\": \"C1\", \"linewidth\": 2},\n",
+    "    label=\"average_model\",\n",
+    ");"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "papermill": {
-     "duration": 0.05969,
-     "end_time": "2020-11-29T12:15:55.884685",
-     "exception": false,
-     "start_time": "2020-11-29T12:15:55.824995",
-     "status": "completed"
-    },
-    "tags": []
-   },
+   "metadata": {},
    "source": [
-    "As we can see the mean value is almost the same for both predictions but the uncertainty in the weighted model is larger. We have effectively propagated the uncertainty about which model we should select to the posterior predictive samples. You can now try with the other two methods for computing weights `stacking` (the default and recommended method) and `pseudo-BMA`.\n",
+    "## To do or not to do?\n",
     "\n",
-    "**Final notes:** \n",
+    "Model averaging is a good idea when you want to improve the robustness of your predictions. Usually a combinations of models will have better predictive performance than any single model. This is specially true when the models are complementary. Something we have not explored in this example is to assign weights to models in a way that they vary for different parts of the data. This can be done as discussed in {cite:t}`Yao_2022`.\n",
     "\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",
+    "When not do to model averaging? Many times we can create new models that effectively work as averages of other models. For instance in this example we could have created a new model that includes all the variables. That's actually a very sensible thing to do. Notice that if a model excludes a variable thats equivalent to setting the coefficient of that variable to zero. If we average a model with the variable and without it, it's like setting the coefficient to a value between zero and the value of the coefficient in the model that includes the variable. This is a very simple example, but the same reasoning applies to more complex models.\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 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 with a prior biased towards 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 would be computing a continuous model that considers 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?"
+    "Hierarchical models are another example were we build a continous version of a model instead of dealing with discrete versions. 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 with a prior biased towards towards tails. We could fit both separate models and then average them. An alternative is to build a hierarchical model to estimate the prior distribution. Instead of contemplating two discrete models, we would be computing a continuous model that considers 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?"
    ]
   },
   {
@@ -1538,7 +1924,8 @@
     "* 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))\n",
-    "* Updated notebook to use pymc 5 by Adrien Porter in November 2023 "
+    "* Updated notebook to use pymc 5 by Adrien Porter in November 2023 \n",
+    "* Updated by Osvaldo Martin in August 2024 "
    ]
   },
   {
@@ -1561,7 +1948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {
     "papermill": {
      "duration": 0.127595,
@@ -1577,19 +1964,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Sun Nov 19 2023\n",
+      "Last updated: Fri Aug 23 2024\n",
       "\n",
       "Python implementation: CPython\n",
-      "Python version       : 3.11.6\n",
-      "IPython version      : 8.17.2\n",
+      "Python version       : 3.11.5\n",
+      "IPython version      : 8.16.1\n",
       "\n",
-      "numpy     : 1.25.2\n",
-      "sys       : 3.11.6 | packaged by conda-forge | (main, Oct  3 2023, 10:37:07) [Clang 15.0.7 ]\n",
-      "pandas    : 2.1.3\n",
-      "pymc      : 5.9.2\n",
-      "arviz     : 0.17.0.dev0\n",
-      "matplotlib: 3.8.1\n",
-      "json      : 2.0.9\n",
+      "arviz     : 0.20.0.dev0\n",
+      "pandas    : 2.1.2\n",
+      "pymc      : 5.16.2+24.g799c98f41\n",
+      "numpy     : 1.24.4\n",
+      "matplotlib: 3.8.4\n",
       "\n",
       "Watermark: 2.4.3\n",
       "\n"
@@ -1626,7 +2011,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/diagnostics_and_criticism/model_averaging.myst.md b/examples/diagnostics_and_criticism/model_averaging.myst.md
index 4499bdacb..648cdbe8b 100644
--- a/examples/diagnostics_and_criticism/model_averaging.myst.md
+++ b/examples/diagnostics_and_criticism/model_averaging.myst.md
@@ -13,7 +13,7 @@ kernelspec:
 (model_averaging)=
 # Model Averaging
 
-:::{post} Aug 2022
+:::{post} Aug 2024
 :tags: model comparison, model averaging
 :category: intermediate
 :author: Osvaldo Martin
@@ -28,6 +28,8 @@ papermill:
   start_time: '2020-11-29T12:13:02.878264'
   status: completed
 ---
+import os
+
 import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
@@ -46,61 +48,63 @@ papermill:
   start_time: '2020-11-29T12:13:07.836201'
   status: completed
 ---
-RANDOM_SEED = 8927
-np.random.seed(RANDOM_SEED)
+rng = np.random.seed(2741)
 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"}}
 
-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 by 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 somewhat similar to computing the full posterior and then just keeping 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. 
+When confronted with more than one model we have several options. One of them is to perform model selection as exemplified by the PyMC examples {ref}`pymc:model_comparison` and the {ref}`GLM-model-selection`, usually is a good idea to also include posterior predictive checks in order to decide which model to keep. Discarding all models except one is equivalent to affirm that, among the evaluated models, one is correct (under some criteria) with probability 1 and the rest are incorrect. In most cases this will be an overstatment that ignores the uncertainty we have in our models. This is somewhat similar to computing the full posterior and then just keeping 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 to consider 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.
+An alternative to this dilema is to perform model selection but to acknoledge the models we discared. If the number of models are not that large this can be part of a technical discussion on a paper, presentation, thesis, and so on. If the audience is not technical enough, this may not be a good idea.
 
-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. PyMC includes three methods that will be briefly discussed in this notebook. You will find a more thorough explanation in the work by {cite:t}`Yao_2018`. PyMC integrates with ArviZ for model comparison. 
+Yet another alternative, the topic of this example, is to perform model averaging. The idea is to weight each model by its merit and generate predictions from each model, proportional to those weights. There are several ways to do this, including the three methods that will be briefly discussed in this notebook. You will find a more thorough explanation in the work by {cite:t}`Yao_2018` and {cite:t}`Yao_2022`. 
 
 
 ## Pseudo Bayesian model averaging
 
-Bayesian models can be weighted by their marginal likelihood, which 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 sensitive 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, which 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 sensitive 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. Additionally, Bayesian model averaging is flawed in the $\mathcal{M}$-open setting in which the true data-generating process is not one of the candidate models being fit {cite:t}`Yao_2018`. A more robust approach is to compute the  expected log pointwise predictive density (ELPD).
+
+$$
+\sum_i^N \log \int \ p(y_i \mid \theta) \; p(\theta \mid y) d\theta
+$$
+
+where $N$ is the number of data points, $y_i$ is the i-th data point, $\theta$ are the parameters of the model, $p(y_i \mid \theta)$ is the likelihood of the i-th data point given the parameters, and $p(\theta \mid y)$ is the posterior distribution.
+
+Once we have computed the ELPD for each model we can compute weights by doing
+
+$$w_i = \frac {e^{dELPD_i}} {\sum_j^M e^{dELPD_i}}$$
+
+Where $dELPD_i$ is the difference between the model with the best ELPD and the i-th model.
+
+This approach is called pseudo Bayesian model averaging, or Akaike-like weighting and is an heuristic to compute the relative probability of each model (given a fixed set of models). Note that we exponetiate to "revert" the effect of the logarithm in the ELPD formula and the denominator is a normalization term to ensure that the weights sum up to one. With a pinch of salt, we can interpret these weights as the probability of each model explaining the data.
 
-$$w_i = \frac {e^{ - \frac{1}{2} dIC_i }} {\sum_j^M e^{ - \frac{1}{2} dIC_j }}$$
+So far so good, but the ELPD is a theoretical quantity, and in practice we need to approximate it. To do so ArviZ offers two methods
 
-Where $dIC_i$ is the difference between the i-th information criterion value and the lowest one. Remember that the lower the value of the IC, the better. We can use any information criterion we want to compute a set of weights, but, of course, we cannot mix them. 
+* WAIC, Widely Applicable Information Criterion
+* LOO, Pareto-Smooth-Leave-One-Out-Cross-Validation.
 
-This approach is called pseudo Bayesian model averaging, or Akaike-like weighting and is an heuristic way to compute the relative probability of each model (given a fixed set of models) from the information criteria values. Note that the denominator is just a normalization term to ensure that the weights sum up to one.
+Both requiere and InferenceData with the log-likelihood group and are equally fast to compute. We recommend using LOO because it has better practical properties, and better diagnostics (so we known when we are having issues with the ELPD estimation).
 
 ## Pseudo Bayesian model averaging with Bayesian Bootstrapping
 
-The above formula for computing weights is a nice and simple approach, but with one major caveat: it does not take into account the uncertainty in the computation of the IC. We could compute the standard error of the IC (assuming a Gaussian approximation) and modify the above formula accordingly. Or we can do something more robust, like using a [Bayesian Bootstrapping](http://www.sumsar.net/blog/2015/04/the-non-parametric-bootstrap-as-a-bayesian-model/) to estimate, and incorporate this uncertainty.
+The above formula for computing weights is a nice and simple approach, but with one major caveat: it does not take into account the uncertainty in the computation of the ELPD. We could compute the standard error of the ELPD value (assuming a Gaussian approximation) and modify the above formula accordingly. Or we can do something more robust, like using a [Bayesian Bootstrapping](http://www.sumsar.net/blog/2015/04/the-non-parametric-bootstrap-as-a-bayesian-model/) to estimate, and incorporate this uncertainty.
 
 ## Stacking
 
-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:
+The third approach we will discuss 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:
 
-$$\max_{w} \frac{1}{n} \sum_{i=1}^{n}log\sum_{k=1}^{K} w_k p(y_i|y_{-i}, M_k)$$
+$$\max_{w} \frac{1}{n} \sum_{i=1}^{n}log\sum_{k=1}^{K} w_k p(y_i \mid y_{-i}, M_k)$$
 
 Where $n$ is the number of data points and $K$ the number of models. To enforce a solution we constrain $w$ to be $w_k \ge 0$ and  $\sum_{k=1}^{K} w_k = 1$. 
 
-The quantity $p(y_i|y_{-i}, M_k)$ is the leave-one-out predictive distribution for the $M_k$ model. Computing it requires fitting each model $n$ times, each time leaving out one data point. Fortunately we can approximate the exact leave-one-out predictive distribution using LOO (or even WAIC), and that is what we do in practice.
+The quantity $p(y_i \mid y_{-i}, M_k)$ is the leave-one-out predictive distribution for the $M_k$ model. Computing it requires fitting each model $n$ times, each time leaving out one data point. Fortunately, this is exactly what LOO approximates in a very efficient way. So we can use LOO and stacking together. To be fair, we can also use WAIC, even when WAIC approximates the ELPD in a different way.
 
 ## Weighted posterior predictive samples
 
-Once we have computed the weights, using any of the above 3 methods,  we can use them to get weighted posterior predictive samples. PyMC offers functions to perform these steps in a simple way, so let's 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 weighted posterior predictive samples. We will illustrate how to do it using the body fat dataset {cite}`penrose1985`. This dataset has measurements from 251 individuals, including their weight, height, the circumference of the abdomen, the circumference of the wrist etc. Our purpose is to predict the percentage of body fat, as estimated by the siri variable, also available from the dataset.
 
-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. To try to give an answer we will use 3 variables:
-* two predictor variables - the proportion of neocortex mass compared to the total mass of the brain, and the logarithm of the body mass of the mothers. 
-* one 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
-3. A model using both variables
-
-Let start by uploading the data and centering the `neocortex` and `log mass` variables, for better sampling.
+Let's start by loading the data
 
 ```{code-cell} ipython3
 ---
@@ -111,237 +115,93 @@ papermill:
   start_time: '2020-11-29T12:13:08.081202'
   status: completed
 ---
-d = pd.read_csv(
-    "https://raw.githubusercontent.com/pymc-devs/resources/master/Rethinking_2/Data/milk.csv",
-    sep=";",
-)
-d = d[["kcal.per.g", "neocortex.perc", "mass"]].rename({"neocortex.perc": "neocortex"}, axis=1)
-d["log_mass"] = np.log(d["mass"])
-d = d[~d.isna().any(axis=1)].drop("mass", axis=1)
-d.iloc[:, 1:] = d.iloc[:, 1:] - d.iloc[:, 1:].mean()
+try:
+    d = pd.read_csv(os.path.join("..", "data", "body_fat.csv"))
+except FileNotFoundError:
+    d = pd.read_csv(pm.get_data("body_fat.csv"))
+
 d.head()
 ```
 
 +++ {"papermill": {"duration": 0.048113, "end_time": "2020-11-29T12:13:09.292526", "exception": false, "start_time": "2020-11-29T12:13:09.244413", "status": "completed"}}
 
-Now that we have the data we are going to build our first model using only the `neocortex`.
+Now that we have the data we are going to build two models, both are simple linear regressions the difference is that for the first one we are going to use the variables `abdomen`, and for the second one we are going to use the variables `wrist`, `height` and `weight`.
 
 ```{code-cell} ipython3
----
-papermill:
-  duration: 75.962348
-  end_time: '2020-11-29T12:14:25.303027'
-  exception: false
-  start_time: '2020-11-29T12:13:09.340679'
-  status: completed
----
 with pm.Model() as model_0:
-    alpha = pm.Normal("alpha", mu=0, sigma=10)
-    beta = pm.Normal("beta", mu=0, sigma=10)
-    sigma = pm.HalfNormal("sigma", 10)
-
-    mu = alpha + beta * d["neocortex"]
-
-    kcal = pm.Normal("kcal", mu=mu, sigma=sigma, observed=d["kcal.per.g"])
-    trace_0 = pm.sample(2000, idata_kwargs={"log_likelihood": True})
-    pm.sample_posterior_predictive(trace_0, extend_inferencedata=True)
-```
-
-+++ {"papermill": {"duration": 0.049578, "end_time": "2020-11-29T12:14:25.401979", "exception": false, "start_time": "2020-11-29T12:14:25.352401", "status": "completed"}}
-
-The second model is exactly the same as the first one, except we now use the logarithm of the mass
-
-```{code-cell} ipython3
----
-papermill:
-  duration: 8.996265
-  end_time: '2020-11-29T12:14:34.447153'
-  exception: false
-  start_time: '2020-11-29T12:14:25.450888'
-  status: completed
----
-with pm.Model() as model_1:
-    alpha = pm.Normal("alpha", mu=0, sigma=10)
+    alpha = pm.Normal("alpha", mu=0, sigma=1)
     beta = pm.Normal("beta", mu=0, sigma=1)
-    sigma = pm.HalfNormal("sigma", 10)
-
-    mu = alpha + beta * d["log_mass"]
-
-    kcal = pm.Normal("kcal", mu=mu, sigma=sigma, observed=d["kcal.per.g"])
-
-    trace_1 = pm.sample(2000, idata_kwargs={"log_likelihood": True})
-    pm.sample_posterior_predictive(trace_1, extend_inferencedata=True)
-```
-
-+++ {"papermill": {"duration": 0.049839, "end_time": "2020-11-29T12:14:34.547268", "exception": false, "start_time": "2020-11-29T12:14:34.497429", "status": "completed"}}
-
-And finally the third model using the `neocortex` and `log_mass` variables
-
-```{code-cell} ipython3
----
-papermill:
-  duration: 19.373847
-  end_time: '2020-11-29T12:14:53.971081'
-  exception: false
-  start_time: '2020-11-29T12:14:34.597234'
-  status: completed
----
-with pm.Model() as model_2:
-    alpha = pm.Normal("alpha", mu=0, sigma=10)
-    beta = pm.Normal("beta", mu=0, sigma=1, shape=2)
-    sigma = pm.HalfNormal("sigma", 10)
+    sigma = pm.HalfNormal("sigma", 5)
 
-    mu = alpha + pm.math.dot(beta, d[["neocortex", "log_mass"]].T)
+    mu = alpha + beta * d["abdomen"]
 
-    kcal = pm.Normal("kcal", mu=mu, sigma=sigma, observed=d["kcal.per.g"])
+    siri = pm.Normal("siri", mu=mu, sigma=sigma, observed=d["siri"])
 
-    trace_2 = pm.sample(2000, idata_kwargs={"log_likelihood": True})
-    pm.sample_posterior_predictive(trace_2, extend_inferencedata=True)
+    idata_0 = pm.sample(idata_kwargs={"log_likelihood": True}, random_seed=rng)
+    pm.sample_posterior_predictive(idata_0, extend_inferencedata=True, random_seed=rng)
 ```
 
-+++ {"papermill": {"duration": 0.050236, "end_time": "2020-11-29T12:14:54.072799", "exception": false, "start_time": "2020-11-29T12:14:54.022563", "status": "completed"}}
-
-Now that we have sampled the posterior for the 3 models, we are going to compare them visually. One option is to use the `forestplot` function that supports plotting more than one trace.
-
 ```{code-cell} ipython3
----
-papermill:
-  duration: 0.967337
-  end_time: '2020-11-29T12:14:55.090748'
-  exception: false
-  start_time: '2020-11-29T12:14:54.123411'
-  status: completed
----
-traces = [trace_0, trace_1, trace_2]
-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"}}
-
-Another option is to plot several traces in a same plot is to use `plot_density`. This plot is somewhat 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).
+with pm.Model() as model_1:
+    alpha = pm.Normal("alpha", mu=0, sigma=1)
+    beta = pm.Normal("beta", mu=0, sigma=1, shape=3)
+    sigma = pm.HalfNormal("sigma", 5)
 
-```{code-cell} ipython3
----
-papermill:
-  duration: 2.61715
-  end_time: '2020-11-29T12:14:57.866426'
-  exception: false
-  start_time: '2020-11-29T12:14:55.249276'
-  status: completed
----
-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)"],
-)
+    mu = alpha + pm.math.dot(beta, d[["wrist", "height", "weight"]].T)
 
-ax[0, 0].set_xlabel("Density")
-ax[0, 0].set_ylabel("")
-ax[0, 0].set_title("95% Credible Intervals: alpha")
+    siri = pm.Normal("siri", mu=mu, sigma=sigma, observed=d["siri"])
 
-ax[0, 1].set_xlabel("Density")
-ax[0, 1].set_ylabel("")
-ax[0, 1].set_title("95% Credible Intervals: sigma")
+    idata_1 = pm.sample(idata_kwargs={"log_likelihood": True}, random_seed=rng)
+    pm.sample_posterior_predictive(idata_1, extend_inferencedata=True, random_seed=rng)
 ```
 
-+++ {"papermill": {"duration": 0.055089, "end_time": "2020-11-29T12:14:57.977616", "exception": false, "start_time": "2020-11-29T12:14:57.922527", "status": "completed"}}
+Before LOO (or WAIC) to compare and or average models we should check that we do not have sampling issues and posterior predictive checks are resonable. For the sake of brevity we are going to skip these steps and instead jump to the model averaging.
 
-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.
+First we need to call `az.compare` to compute the LOO values for each model and the weights using `stacking`. These are the default options, if you want to perform pseudo Bayesian model averaging you can use the `method='BB-pseudo-BMA'` that includes the Bayesian Bootstrap estimation of the uncertainty in the ELPD.
 
 ```{code-cell} ipython3
----
-papermill:
-  duration: 0.239084
-  end_time: '2020-11-29T12:14:58.272998'
-  exception: false
-  start_time: '2020-11-29T12:14:58.033914'
-  status: completed
----
-model_dict = dict(zip(["model_0", "model_1", "model_2"], traces))
+model_dict = dict(zip(["model_0", "model_1"], [idata_0, idata_1]))
 comp = az.compare(model_dict)
 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"}}
-
-We can see that the best model is `model_2`, the one with both predictor variables. Note that the DataFrame is ordered from lowest to highest WAIC (_i.e_ from _best_ 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. It 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 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 ArviZ we can call the `az.stats.weight_predictions` function as follows:
+We can see from the column `weight`, that `model_1` has a weight of $\approx 0.6$ and `model_2` has a weight $\approx 0.4$. To use this weights to generate posterior predictive samples we can use the `az.weighted_posterior` function. This function takes the InferenceData objects and the weights and returns a new InferenceData object.
 
 ```{code-cell} ipython3
----
-papermill:
-  duration: 31.463179
-  end_time: '2020-11-29T12:15:29.907492'
-  exception: false
-  start_time: '2020-11-29T12:14:58.444313'
-  status: completed
----
-ppc_w = az.stats.weight_predictions(
+ppc_w = az.weight_predictions(
     [model_dict[name] for name in comp.index],
     weights=comp.weight,
-).posterior_predictive
+)
 ppc_w
 ```
 
-+++ {"papermill": {"duration": 0.058454, "end_time": "2020-11-29T12:15:30.024455", "exception": false, "start_time": "2020-11-29T12:15:29.966001", "status": "completed"}}
-
-We are also going to name the PPC for the lowest-WAIC model.
-
-```{code-cell} ipython3
----
-papermill:
-  duration: 25.204481
-  end_time: '2020-11-29T12:15:55.287049'
-  exception: false
-  start_time: '2020-11-29T12:15:30.082568'
-  status: completed
----
-ppc_2 = trace_2.posterior_predictive
-```
-
-+++ {"papermill": {"duration": 0.058214, "end_time": "2020-11-29T12:15:55.404271", "exception": false, "start_time": "2020-11-29T12:15:55.346057", "status": "completed"}}
-
-A simple way to compare both kind of predictions is to plot their mean and hpd interval.
+From the following plot we can see that the avearged model is a combination of the two models.
 
 ```{code-cell} ipython3
----
-papermill:
-  duration: 0.301319
-  end_time: '2020-11-29T12:15:55.764128'
-  exception: false
-  start_time: '2020-11-29T12:15:55.462809'
-  status: completed
----
-mean_w = ppc_w["kcal"].mean()
-hpd_w = az.hdi(ppc_w, var_names="kcal", input_core_dims=[["sample", "kcal_dim_2"]])
-
-mean = ppc_2["kcal"].mean()
-hpd = az.hdi(ppc_2, var_names="kcal", input_core_dims=[["chain", "draw", "kcal_dim_2"]])
-
-plt.plot(mean_w, 1, "C0o", label="weighted models")
-plt.hlines(1, *hpd_w["kcal"], colors="C0")
-plt.plot(mean, 0, "C1o", label="model 2")
-plt.hlines(0, *hpd["kcal"], "C1")
-
-plt.yticks([])
-plt.ylim(-1, 2)
-plt.xlabel("kcal per g")
-plt.legend();
+az.plot_kde(
+    idata_0.posterior_predictive["siri"].values,
+    plot_kwargs={"color": "C0", "linestyle": "--"},
+    label="model_0",
+)
+az.plot_kde(
+    idata_1.posterior_predictive["siri"].values,
+    plot_kwargs={"color": "C0", "linestyle": "--"},
+    label="model_1",
+)
+az.plot_kde(
+    ppc_w.posterior_predictive["siri"].values,
+    plot_kwargs={"color": "C1", "linewidth": 2},
+    label="average_model",
+);
 ```
 
-+++ {"papermill": {"duration": 0.05969, "end_time": "2020-11-29T12:15:55.884685", "exception": false, "start_time": "2020-11-29T12:15:55.824995", "status": "completed"}}
-
-As we can see the mean value is almost the same for both predictions but the uncertainty in the weighted model is larger. We have effectively propagated the uncertainty about which model we should select to the posterior predictive samples. You can now try with the other two methods for computing weights `stacking` (the default and recommended method) and `pseudo-BMA`.
+## To do or not to do?
 
-**Final notes:** 
+Model averaging is a good idea when you want to improve the robustness of your predictions. Usually a combinations of models will have better predictive performance than any single model. This is specially true when the models are complementary. Something we have not explored in this example is to assign weights to models in a way that they vary for different parts of the data. This can be done as discussed in {cite:t}`Yao_2022`.
 
-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.
+When not do to model averaging? Many times we can create new models that effectively work as averages of other models. For instance in this example we could have created a new model that includes all the variables. That's actually a very sensible thing to do. Notice that if a model excludes a variable thats equivalent to setting the coefficient of that variable to zero. If we average a model with the variable and without it, it's like setting the coefficient to a value between zero and the value of the coefficient in the model that includes the variable. This is a very simple example, but the same reasoning applies to more complex models.
 
-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 with a prior biased towards 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 would be computing a continuous model that considers 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?
+Hierarchical models are another example were we build a continous version of a model instead of dealing with discrete versions. 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 with a prior biased towards towards tails. We could fit both separate models and then average them. An alternative is to build a hierarchical model to estimate the prior distribution. Instead of contemplating two discrete models, we would be computing a continuous model that considers 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?
 
 +++
 
@@ -354,6 +214,7 @@ Besides averaging discrete models, we can sometimes think of continuous versions
 * 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))
 * Updated notebook to use pymc 5 by Adrien Porter in November 2023 
+* Updated by Osvaldo Martin in August 2024 
 
 +++
 
diff --git a/examples/references.bib b/examples/references.bib
index 50e2b4301..fccb953bb 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -895,3 +895,33 @@ @article{solin2020Hilbert
 	volume = {30},
 	year = {2020},
 }
+
+@article{penrose1985,
+	title = {{GENERALIZED} {BODY} {COMPOSITION} {PREDICTION} {EQUATION} {FOR} {MEN} {USING} {SIMPLE} {MEASUREMENT} {TECHNIQUES}},
+	volume = {17},
+	issn = {0195-9131},
+	url = {https://journals.lww.com/acsm-msse/citation/1985/04000/generalized_body_composition_prediction_equation.37.aspx},
+	abstract = {An abstract is unavailable. This article is available as a PDF only.},
+	language = {en-US},
+	number = {2},
+	urldate = {2023-10-17},
+	journal = {Medicine \& Science in Sports \& Exercise},
+	author = {Penrose, K. W. and Nelson, A. G. and Fisher, A. G.},
+	month = apr,
+	year = {1985},
+	pages = {189},
+}
+
+@article{Yao_2022,
+author = {Yuling Yao and Gregor Pirš and Aki Vehtari and Andrew Gelman},
+title = {{Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful}},
+volume = {17},
+journal = {Bayesian Analysis},
+number = {4},
+publisher = {International Society for Bayesian Analysis},
+pages = {1043 -- 1071},
+keywords = {Bayesian hierarchical modeling, conditional prediction, covariate shift, model averaging, prior construction, stacking},
+year = {2022},
+doi = {10.1214/21-BA1287},
+URL = {https://doi.org/10.1214/21-BA1287}
+}