From c155dd610ca6816311c6f8a790935b8632dad297 Mon Sep 17 00:00:00 2001
From: aloctavodia <aloctavodia@gmail.com>
Date: Thu, 1 Aug 2024 17:22:42 -0300
Subject: [PATCH] update to pymc

---
 .../model_averaging.ipynb                     | 516 ++++++++++--------
 .../model_averaging.myst.md                   |  79 +--
 2 files changed, 320 insertions(+), 275 deletions(-)

diff --git a/examples/diagnostics_and_criticism/model_averaging.ipynb b/examples/diagnostics_and_criticism/model_averaging.ipynb
index 7b871c0ab..eb63e6313 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",
@@ -32,7 +32,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on PyMC3 v3.11.5\n"
+      "Running on PyMC v5.16.2+11.gb407c01ac\n"
      ]
     }
    ],
@@ -41,9 +41,9 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pymc3 as pm\n",
+    "import pymc as pm\n",
     "\n",
-    "print(f\"Running on PyMC3 v{pm.__version__}\")"
+    "print(f\"Running on PyMC v{pm.__version__}\")"
    ]
   },
   {
@@ -119,10 +119,10 @@
     "Briefly, our problem is as follows: We want to explore the composition of milk across several primate species, it is hypothesized that females from species of primates with larger brains produce more _nutritious_ milk (loosely speaking this is done _in order to_ support the development of such big brains). This is an important question for evolutionary biologists and try to give an answer we will use 3 variables, two predictor variables: the proportion of neocortex compare to the total mass of the brain and the logarithm of the body mass of the mothers. And for predicted variable, the kilocalories per gram of milk. With these variables we are going to build 3 different linear models:\n",
     " \n",
     "1. A model using only the neocortex variable\n",
-    "2. A model using only the logarithm of the mass variable\n",
+    "2. A model using only 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 start by uploading the data and centering the `neocortex` and `mass` variables, for better sampling."
    ]
   },
   {
@@ -162,7 +162,7 @@
        "      <th></th>\n",
        "      <th>kcal.per.g</th>\n",
        "      <th>neocortex</th>\n",
-       "      <th>log_mass</th>\n",
+       "      <th>mass</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -170,43 +170,43 @@
        "      <th>0</th>\n",
        "      <td>0.49</td>\n",
        "      <td>-12.415882</td>\n",
-       "      <td>-0.831486</td>\n",
+       "      <td>-14.687647</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",
+       "      <td>-11.387647</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",
+       "      <td>-11.267647</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",
+       "      <td>-14.127647</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",
+       "      <td>-15.957647</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"
+       "   kcal.per.g  neocortex       mass\n",
+       "0        0.49 -12.415882 -14.687647\n",
+       "5        0.47  -3.035882 -11.387647\n",
+       "6        0.56  -3.035882 -11.267647\n",
+       "7        0.89   0.064118 -14.127647\n",
+       "9        0.92   1.274118 -15.957647"
       ]
      },
      "execution_count": 3,
@@ -220,8 +220,7 @@
     "    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 = d[~d.isna().any(axis=1)]  # .drop(\"mass\", axis=1)\n",
     "d.iloc[:, 1:] = d.iloc[:, 1:] - d.iloc[:, 1:].mean()\n",
     "d.head()"
    ]
@@ -262,29 +261,19 @@
      "text": [
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [sigma, beta, alpha]\n"
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, beta, sigma]\n"
      ]
     },
     {
      "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8576e3273ac45adb305a1087ab1125c",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "Output()"
       ]
      },
      "metadata": {},
@@ -293,15 +282,21 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [6000/6000 00:04<00:00 Sampling 2 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": {},
@@ -311,20 +306,60 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 12 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n",
+      "Sampling: [kcal]\n"
      ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d94bdb635f044a2ebe101b74d9091120",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "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": [
+       "\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",
+    "    alpha = pm.Normal(\"alpha\", mu=0, sigma=1)\n",
+    "    beta = pm.Normal(\"beta\", mu=0, sigma=1)\n",
+    "    sigma = pm.HalfNormal(\"sigma\", 1)\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, return_inferencedata=True)"
+    "\n",
+    "    idata_0 = pm.sample(idata_kwargs={\"log_likelihood\": True})\n",
+    "    idata_0.extend(pm.sample_posterior_predictive(idata_0))"
    ]
   },
   {
@@ -363,29 +398,19 @@
      "text": [
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [sigma, beta, alpha]\n"
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, beta, sigma]\n"
      ]
     },
     {
      "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c736de1cd6ac4a398a4f57cc54151f9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "Output()"
       ]
      },
      "metadata": {},
@@ -394,15 +419,21 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [6000/6000 00:04<00:00 Sampling 2 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": {},
@@ -412,22 +443,60 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 11 seconds.\n",
-      "The acceptance probability does not match the target. It is 0.8826043398520717, but should be close to 0.8. Try to increase the number of tuning steps.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 5 seconds.\n",
+      "Sampling: [kcal]\n"
      ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df632856ad144b98a5aa580cc55e4119",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "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": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "with pm.Model() as model_1:\n",
-    "    alpha = pm.Normal(\"alpha\", mu=0, sigma=10)\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\", 1)\n",
     "\n",
-    "    mu = alpha + beta * d[\"log_mass\"]\n",
+    "    mu = alpha + beta * d[\"mass\"]\n",
     "\n",
     "    kcal = pm.Normal(\"kcal\", mu=mu, sigma=sigma, observed=d[\"kcal.per.g\"])\n",
     "\n",
-    "    trace_1 = pm.sample(2000, return_inferencedata=True)"
+    "    idata_1 = pm.sample(idata_kwargs={\"log_likelihood\": True})\n",
+    "    idata_1.extend(pm.sample_posterior_predictive(idata_1))"
    ]
   },
   {
@@ -443,7 +512,7 @@
     "tags": []
    },
    "source": [
-    "And finally the third model using the `neocortex` and `log_mass` variables"
+    "And finally the third model using the `neocortex` and `mass` variables"
    ]
   },
   {
@@ -466,29 +535,19 @@
      "text": [
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [sigma, beta, alpha]\n"
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, beta, sigma]\n"
      ]
     },
     {
      "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e773bc8b5134b2ba55573a625951c1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "Output()"
       ]
      },
      "metadata": {},
@@ -497,15 +556,21 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [6000/6000 00:06<00:00 Sampling 2 chains, 0 divergences]\n",
-       "    </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": {},
@@ -515,21 +580,60 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 12 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 5 seconds.\n",
+      "Sampling: [kcal]\n"
      ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b52e00cff02c435e9b09fddf0fb9dca1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "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": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "with pm.Model() as model_2:\n",
-    "    alpha = pm.Normal(\"alpha\", mu=0, sigma=10)\n",
+    "    alpha = pm.Normal(\"alpha\", mu=0, sigma=1)\n",
     "    beta = pm.Normal(\"beta\", mu=0, sigma=1, shape=2)\n",
-    "    sigma = pm.HalfNormal(\"sigma\", 10)\n",
+    "    sigma = pm.HalfNormal(\"sigma\", 1)\n",
     "\n",
-    "    mu = alpha + pm.math.dot(beta, d[[\"neocortex\", \"log_mass\"]].T)\n",
+    "    mu = alpha + pm.math.dot(beta, d[[\"neocortex\", \"mass\"]].T)\n",
     "\n",
     "    kcal = pm.Normal(\"kcal\", mu=mu, sigma=sigma, observed=d[\"kcal.per.g\"])\n",
     "\n",
-    "    trace_2 = pm.sample(2000, return_inferencedata=True)"
+    "    idata_2 = pm.sample(idata_kwargs={\"log_likelihood\": True})\n",
+    "    idata_2.extend(pm.sample_posterior_predictive(idata_2))"
    ]
   },
   {
@@ -564,20 +668,18 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "traces = [trace_0, trace_1, trace_2]\n",
-    "az.plot_forest(traces, figsize=(10, 5));"
+    "idatas = [idata_0, idata_1, idata_2]\n",
+    "az.plot_forest(idatas, figsize=(10, 5));"
    ]
   },
   {
@@ -612,33 +714,21 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "Text(0.5, 1.0, '95% Credible Intervals: sigma')"
+       "<Figure size 1656x552 with 2 Axes>"
       ]
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 993.6x331.2 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "ax = az.plot_density(\n",
-    "    traces,\n",
+    "    idatas,\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",
+    "    data_labels=[\"Model 0 (neocortex)\", \"Model 1 (mass)\", \"Model 2 (neocortex+mass)\"],\n",
     ")\n",
     "\n",
     "ax[0, 0].set_xlabel(\"Density\")\n",
@@ -647,7 +737,7 @@
     "\n",
     "ax[0, 1].set_xlabel(\"Density\")\n",
     "ax[0, 1].set_ylabel(\"\")\n",
-    "ax[0, 1].set_title(\"95% Credible Intervals: sigma\")"
+    "ax[0, 1].set_title(\"95% Credible Intervals: sigma\");"
    ]
   },
   {
@@ -663,7 +753,7 @@
     "tags": []
    },
    "source": [
-    "Now that we have sampled the posterior for the 3 models, we are going to use WAIC (Widely applicable information criterion) to compare the 3 models. We can do this using the `compare` function included with ArviZ."
+    "Now that we have sampled the posterior for the 3 models, we are going to compare the 3 models. We can do this using the `compare` function included with ArviZ. By default `compare` used the `LOO` method and the weights using `stacking`."
    ]
   },
   {
@@ -702,25 +792,25 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>rank</th>\n",
-       "      <th>loo</th>\n",
+       "      <th>elpd_loo</th>\n",
        "      <th>p_loo</th>\n",
-       "      <th>d_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>loo_scale</th>\n",
+       "      <th>scale</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>model_2</th>\n",
        "      <td>0</td>\n",
-       "      <td>8.365702</td>\n",
-       "      <td>3.159733</td>\n",
+       "      <td>5.872264</td>\n",
+       "      <td>2.818042</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>2.523747</td>\n",
+       "      <td>9.396734e-01</td>\n",
+       "      <td>1.345685</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>False</td>\n",
        "      <td>log</td>\n",
@@ -728,24 +818,24 @@
        "    <tr>\n",
        "      <th>model_1</th>\n",
        "      <td>1</td>\n",
-       "      <td>4.485976</td>\n",
-       "      <td>2.017674</td>\n",
-       "      <td>3.879726</td>\n",
-       "      <td>5.162537e-15</td>\n",
-       "      <td>2.054953</td>\n",
-       "      <td>1.705681</td>\n",
+       "      <td>4.573080</td>\n",
+       "      <td>1.924629</td>\n",
+       "      <td>1.299183</td>\n",
+       "      <td>6.032655e-02</td>\n",
+       "      <td>1.718065</td>\n",
+       "      <td>1.656519</td>\n",
        "      <td>False</td>\n",
        "      <td>log</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>model_0</th>\n",
        "      <td>2</td>\n",
-       "      <td>3.419154</td>\n",
-       "      <td>2.078996</td>\n",
-       "      <td>4.946548</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.577262</td>\n",
-       "      <td>2.453546</td>\n",
+       "      <td>3.599971</td>\n",
+       "      <td>1.929488</td>\n",
+       "      <td>2.272292</td>\n",
+       "      <td>6.661338e-16</td>\n",
+       "      <td>1.592850</td>\n",
+       "      <td>1.339484</td>\n",
        "      <td>False</td>\n",
        "      <td>log</td>\n",
        "    </tr>\n",
@@ -754,15 +844,15 @@
        "</div>"
       ],
       "text/plain": [
-       "         rank       loo     p_loo     d_loo        weight        se       dse  \\\n",
-       "model_2     0  8.365702  3.159733  0.000000  1.000000e+00  2.523747  0.000000   \n",
-       "model_1     1  4.485976  2.017674  3.879726  5.162537e-15  2.054953  1.705681   \n",
-       "model_0     2  3.419154  2.078996  4.946548  0.000000e+00  1.577262  2.453546   \n",
+       "         rank  elpd_loo     p_loo  elpd_diff        weight        se  \\\n",
+       "model_2     0  5.872264  2.818042   0.000000  9.396734e-01  1.345685   \n",
+       "model_1     1  4.573080  1.924629   1.299183  6.032655e-02  1.718065   \n",
+       "model_0     2  3.599971  1.929488   2.272292  6.661338e-16  1.592850   \n",
        "\n",
-       "         warning loo_scale  \n",
-       "model_2    False       log  \n",
-       "model_1    False       log  \n",
-       "model_0    False       log  "
+       "              dse  warning scale  \n",
+       "model_2  0.000000    False   log  \n",
+       "model_1  1.656519    False   log  \n",
+       "model_0  1.339484    False   log  "
       ]
      },
      "execution_count": 9,
@@ -771,7 +861,7 @@
     }
    ],
    "source": [
-    "model_dict = dict(zip([\"model_0\", \"model_1\", \"model_2\"], traces))\n",
+    "model_dict = dict(zip([\"model_0\", \"model_1\", \"model_2\"], idatas))\n",
     "comp = az.compare(model_dict)\n",
     "comp"
    ]
@@ -789,7 +879,7 @@
     "tags": []
    },
    "source": [
-    "We can see that the best model is `model_2`, the one with both predictor variables. Notice the DataFrame is ordered from lowest to highest WAIC (_i.e_ from _better_ to _worst_ model). Check the {ref}`pymc:model_comparison` for a more detailed discussion on model comparison.\n",
+    "We can see that the best model is `model_2`, the one with both predictor variables. Notice the DataFrame is ordered from the highest value of ELPD (_i.e_ from _better_ to _worst_ model). Check the {ref}`pymc:model_comparison` for a more detailed discussion on model comparison.\n",
     "\n",
     "We can also see that we get a column with the relative `weight` for each model (according to the first equation at the beginning of this notebook). This weights can be _vaguely_ interpreted as the probability that each model will make the correct predictions on future data. Of course this interpretation is conditional on the models used to compute the weights, if we add or remove models the weights will change. And also is dependent on the assumptions behind WAIC (or any other Information Criterion used). So try to not overinterpret these `weights`. \n",
     "\n",
@@ -809,56 +899,11 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='4000' class='' max='4000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [4000/4000 00:06<00:00]\n",
-       "    </div>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ppc_w = pm.sample_posterior_predictive_w(\n",
-    "    traces=traces,\n",
-    "    models=[model_0, model_1, model_2],\n",
-    "    weights=comp.weight.sort_index(ascending=True),\n",
-    "    progressbar=True,\n",
-    ")"
+    "ppc_w = az.weight_predictions(\n",
+    "    [idata_2, idata_1, idata_0], weights=list(comp.weight.values)\n",
+    ").posterior_predictive"
    ]
   },
   {
@@ -874,9 +919,9 @@
     "tags": []
    },
    "source": [
-    "Notice that we are passing the weights ordered by their index. We are doing this because we pass `traces` and `models` ordered from model 0 to 2, but the computed weights are ordered from lowest to highest WAIC (or equivalently from larger to lowest weight). In summary, we must be sure that we are correctly pairing the weights and models.\n",
+    "Notice that the order of the weights and idatas match the order in the `comp` DataFrame.\n",
     "\n",
-    "We are also going to compute PPCs for the lowest-WAIC model."
+    "We are also going to extract the posterior predictive samples for the model with the highest ELPD."
    ]
   },
   {
@@ -894,7 +939,7 @@
    },
    "outputs": [],
    "source": [
-    "ppc_2 = pm.sample_posterior_predictive(trace=trace_2, model=model_2, progressbar=False)"
+    "ppc_2 = az.extract(idata_2, group=\"posterior_predictive\")"
    ]
   },
   {
@@ -929,23 +974,21 @@
    "outputs": [
     {
      "data": {
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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 720x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "mean_w = ppc_w[\"kcal\"].mean()\n",
-    "hpd_w = az.hdi(ppc_w[\"kcal\"].flatten())\n",
+    "hpd_w = az.hdi(ppc_w[\"kcal\"].values.flatten())\n",
     "\n",
     "mean = ppc_2[\"kcal\"].mean()\n",
-    "hpd = az.hdi(ppc_2[\"kcal\"].flatten())\n",
+    "hpd = az.hdi(ppc_2[\"kcal\"].values.flatten())\n",
     "\n",
     "plt.plot(mean_w, 1, \"C0o\", label=\"weighted models\")\n",
     "plt.hlines(1, *hpd_w, \"C0\")\n",
@@ -991,7 +1034,8 @@
     "* Updated by Marco Gorelli in November 2020 ([pymc#4271](https://github.com/pymc-devs/pymc/pull/4271))\n",
     "* Moved from pymc to pymc-examples repo in December 2020 ([pymc-examples#8](https://github.com/pymc-devs/pymc-examples/pull/8))\n",
     "* Updated by Raul Maldonado in February 2021 ([pymc#25](https://github.com/pymc-devs/pymc-examples/pull/25))\n",
-    "* Updated Markdown and styling by @reshamas in August 2022, ([pymc-examples#414](https://github.com/pymc-devs/pymc-examples/pull/414))"
+    "* Updated Markdown and styling by @reshamas in August 2022, ([pymc-examples#414](https://github.com/pymc-devs/pymc-examples/pull/414))\n",
+    "* Updated by Osvaldo Martin in Aug 2024"
    ]
   },
   {
@@ -1030,19 +1074,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Sun Aug 21 2022\n",
+      "Last updated: Thu Aug 01 2024\n",
       "\n",
       "Python implementation: CPython\n",
-      "Python version       : 3.10.5\n",
-      "IPython version      : 8.4.0\n",
+      "Python version       : 3.11.5\n",
+      "IPython version      : 8.16.1\n",
       "\n",
-      "pandas    : 1.4.3\n",
-      "matplotlib: 3.5.2\n",
-      "numpy     : 1.22.1\n",
-      "arviz     : 0.12.1\n",
-      "pymc3     : 3.11.5\n",
+      "pymc      : 5.16.2+11.gb407c01ac\n",
+      "pandas    : 2.1.2\n",
+      "arviz     : 0.19.0.dev0\n",
+      "matplotlib: 3.8.4\n",
+      "numpy     : 1.24.4\n",
       "\n",
-      "Watermark: 2.3.1\n",
+      "Watermark: 2.4.3\n",
       "\n"
      ]
     }
@@ -1077,7 +1121,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "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 61a37dda4..d50c9104f 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
@@ -32,9 +32,9 @@ import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import pymc3 as pm
+import pymc as pm
 
-print(f"Running on PyMC3 v{pm.__version__}")
+print(f"Running on PyMC v{pm.__version__}")
 ```
 
 ```{code-cell} ipython3
@@ -93,10 +93,10 @@ The following example is taken from the superb book {cite:t}`mcelreath2018statis
 Briefly, our problem is as follows: We want to explore the composition of milk across several primate species, it is hypothesized that females from species of primates with larger brains produce more _nutritious_ milk (loosely speaking this is done _in order to_ support the development of such big brains). This is an important question for evolutionary biologists and try to give an answer we will use 3 variables, two predictor variables: the proportion of neocortex compare to the total mass of the brain and the logarithm of the body mass of the mothers. And for predicted variable, the kilocalories per gram of milk. With these variables we are going to build 3 different linear models:
  
 1. A model using only the neocortex variable
-2. A model using only the logarithm of the mass variable
+2. A model using only 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 start by uploading the data and centering the `neocortex` and `mass` variables, for better sampling.
 
 ```{code-cell} ipython3
 ---
@@ -112,8 +112,7 @@ d = pd.read_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 = d[~d.isna().any(axis=1)]  # .drop("mass", axis=1)
 d.iloc[:, 1:] = d.iloc[:, 1:] - d.iloc[:, 1:].mean()
 d.head()
 ```
@@ -132,14 +131,16 @@ papermill:
   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)
+    alpha = pm.Normal("alpha", mu=0, sigma=1)
+    beta = pm.Normal("beta", mu=0, sigma=1)
+    sigma = pm.HalfNormal("sigma", 1)
 
     mu = alpha + beta * d["neocortex"]
 
     kcal = pm.Normal("kcal", mu=mu, sigma=sigma, observed=d["kcal.per.g"])
-    trace_0 = pm.sample(2000, return_inferencedata=True)
+
+    idata_0 = pm.sample(idata_kwargs={"log_likelihood": True})
+    idata_0.extend(pm.sample_posterior_predictive(idata_0))
 ```
 
 +++ {"papermill": {"duration": 0.049578, "end_time": "2020-11-29T12:14:25.401979", "exception": false, "start_time": "2020-11-29T12:14:25.352401", "status": "completed"}}
@@ -156,20 +157,21 @@ papermill:
   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)
+    sigma = pm.HalfNormal("sigma", 1)
 
-    mu = alpha + beta * d["log_mass"]
+    mu = alpha + beta * d["mass"]
 
     kcal = pm.Normal("kcal", mu=mu, sigma=sigma, observed=d["kcal.per.g"])
 
-    trace_1 = pm.sample(2000, return_inferencedata=True)
+    idata_1 = pm.sample(idata_kwargs={"log_likelihood": True})
+    idata_1.extend(pm.sample_posterior_predictive(idata_1))
 ```
 
 +++ {"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
+And finally the third model using the `neocortex` and `mass` variables
 
 ```{code-cell} ipython3
 ---
@@ -181,15 +183,16 @@ papermill:
   status: completed
 ---
 with pm.Model() as model_2:
-    alpha = pm.Normal("alpha", mu=0, sigma=10)
+    alpha = pm.Normal("alpha", mu=0, sigma=1)
     beta = pm.Normal("beta", mu=0, sigma=1, shape=2)
-    sigma = pm.HalfNormal("sigma", 10)
+    sigma = pm.HalfNormal("sigma", 1)
 
-    mu = alpha + pm.math.dot(beta, d[["neocortex", "log_mass"]].T)
+    mu = alpha + pm.math.dot(beta, d[["neocortex", "mass"]].T)
 
     kcal = pm.Normal("kcal", mu=mu, sigma=sigma, observed=d["kcal.per.g"])
 
-    trace_2 = pm.sample(2000, return_inferencedata=True)
+    idata_2 = pm.sample(idata_kwargs={"log_likelihood": True})
+    idata_2.extend(pm.sample_posterior_predictive(idata_2))
 ```
 
 +++ {"papermill": {"duration": 0.050236, "end_time": "2020-11-29T12:14:54.072799", "exception": false, "start_time": "2020-11-29T12:14:54.022563", "status": "completed"}}
@@ -205,8 +208,8 @@ papermill:
   start_time: '2020-11-29T12:14:54.123411'
   status: completed
 ---
-traces = [trace_0, trace_1, trace_2]
-az.plot_forest(traces, figsize=(10, 5));
+idatas = [idata_0, idata_1, idata_2]
+az.plot_forest(idatas, 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"}}
@@ -223,10 +226,10 @@ papermill:
   status: completed
 ---
 ax = az.plot_density(
-    traces,
+    idatas,
     var_names=["alpha", "sigma"],
     shade=0.1,
-    data_labels=["Model 0 (neocortex)", "Model 1 (log_mass)", "Model 2 (neocortex+log_mass)"],
+    data_labels=["Model 0 (neocortex)", "Model 1 (mass)", "Model 2 (neocortex+mass)"],
 )
 
 ax[0, 0].set_xlabel("Density")
@@ -235,12 +238,12 @@ ax[0, 0].set_title("95% Credible Intervals: alpha")
 
 ax[0, 1].set_xlabel("Density")
 ax[0, 1].set_ylabel("")
-ax[0, 1].set_title("95% Credible Intervals: sigma")
+ax[0, 1].set_title("95% Credible Intervals: sigma");
 ```
 
 +++ {"papermill": {"duration": 0.055089, "end_time": "2020-11-29T12:14:57.977616", "exception": false, "start_time": "2020-11-29T12:14:57.922527", "status": "completed"}}
 
-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.
+Now that we have sampled the posterior for the 3 models, we are going to compare the 3 models. We can do this using the `compare` function included with ArviZ. By default `compare` used the `LOO` method and the weights using `stacking`.
 
 ```{code-cell} ipython3
 ---
@@ -251,14 +254,14 @@ papermill:
   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", "model_2"], idatas))
 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. Notice the DataFrame is ordered from lowest to highest WAIC (_i.e_ from _better_ to _worst_ model). Check the {ref}`pymc:model_comparison` for a more detailed discussion on model comparison.
+We can see that the best model is `model_2`, the one with both predictor variables. Notice the DataFrame is ordered from the highest value of ELPD (_i.e_ from _better_ to _worst_ model). Check the {ref}`pymc:model_comparison` for a more detailed discussion on model comparison.
 
 We can also see that we get a column with the relative `weight` for each model (according to the first equation at the beginning of this notebook). This weights can be _vaguely_ interpreted as the probability that each model will make the correct predictions on future data. Of course this interpretation is conditional on the models used to compute the weights, if we add or remove models the weights will change. And also is dependent on the assumptions behind WAIC (or any other Information Criterion used). So try to not overinterpret these `weights`. 
 
@@ -273,19 +276,16 @@ papermill:
   start_time: '2020-11-29T12:14:58.444313'
   status: completed
 ---
-ppc_w = pm.sample_posterior_predictive_w(
-    traces=traces,
-    models=[model_0, model_1, model_2],
-    weights=comp.weight.sort_index(ascending=True),
-    progressbar=True,
-)
+ppc_w = az.weight_predictions(
+    [idata_2, idata_1, idata_0], weights=list(comp.weight.values)
+).posterior_predictive
 ```
 
 +++ {"papermill": {"duration": 0.058454, "end_time": "2020-11-29T12:15:30.024455", "exception": false, "start_time": "2020-11-29T12:15:29.966001", "status": "completed"}}
 
-Notice that we are passing the weights ordered by their index. We are doing this because we pass `traces` and `models` ordered from model 0 to 2, but the computed weights are ordered from lowest to highest WAIC (or equivalently from larger to lowest weight). In summary, we must be sure that we are correctly pairing the weights and models.
+Notice that the order of the weights and idatas match the order in the `comp` DataFrame.
 
-We are also going to compute PPCs for the lowest-WAIC model.
+We are also going to extract the posterior predictive samples for the model with the highest ELPD.
 
 ```{code-cell} ipython3
 ---
@@ -296,7 +296,7 @@ papermill:
   start_time: '2020-11-29T12:15:30.082568'
   status: completed
 ---
-ppc_2 = pm.sample_posterior_predictive(trace=trace_2, model=model_2, progressbar=False)
+ppc_2 = az.extract(idata_2, group="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"}}
@@ -313,10 +313,10 @@ papermill:
   status: completed
 ---
 mean_w = ppc_w["kcal"].mean()
-hpd_w = az.hdi(ppc_w["kcal"].flatten())
+hpd_w = az.hdi(ppc_w["kcal"].values.flatten())
 
 mean = ppc_2["kcal"].mean()
-hpd = az.hdi(ppc_2["kcal"].flatten())
+hpd = az.hdi(ppc_2["kcal"].values.flatten())
 
 plt.plot(mean_w, 1, "C0o", label="weighted models")
 plt.hlines(1, *hpd_w, "C0")
@@ -349,6 +349,7 @@ Besides averaging discrete models we can sometimes think of continuous versions
 * Moved from pymc to pymc-examples repo in December 2020 ([pymc-examples#8](https://github.com/pymc-devs/pymc-examples/pull/8))
 * Updated by Raul Maldonado in February 2021 ([pymc#25](https://github.com/pymc-devs/pymc-examples/pull/25))
 * Updated Markdown and styling by @reshamas in August 2022, ([pymc-examples#414](https://github.com/pymc-devs/pymc-examples/pull/414))
+* Updated by Osvaldo Martin in Aug 2024
 
 +++