diff --git a/examples/introductory/api_quickstart.ipynb b/examples/introductory/api_quickstart.ipynb
index 8fcac7c02..875ea0901 100644
--- a/examples/introductory/api_quickstart.ipynb
+++ b/examples/introductory/api_quickstart.ipynb
@@ -1,2349 +1,3298 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "(api_quickstart)=\n",
-    "# General API quickstart\n",
-    "\n",
-    ":::{post} May 31, 2022\n",
-    ":tags: \n",
-    ":category: beginner\n",
-    ":author: Christian Luhmann\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/xian/anaconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pkg_resources/__init__.py:123: PkgResourcesDeprecationWarning: main is an invalid version and will not be supported in a future release\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "import arviz as az\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pymc as pm\n",
-    "import pytensor.tensor as pt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "RANDOM_SEED = 8927\n",
-    "rng = np.random.default_rng(RANDOM_SEED)\n",
-    "az.style.use(\"arviz-darkgrid\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Model creation\n",
-    "\n",
-    "Models in PyMC are centered around the `Model` class. It has references to all random variables (RVs) and computes the model logp and its gradients. Usually, you would instantiate it as part of a `with` context:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with pm.Model() as model:\n",
-    "    # Model definition\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We discuss RVs further below but let's create a simple model to explore the `Model` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with pm.Model() as model:\n",
-    "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
-    "    obs = pm.Normal(\"obs\", mu=mu, sigma=1, observed=rng.standard_normal(100))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[mu ~ N(0, 1), obs ~ N(mu, 1)]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.basic_RVs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[mu ~ N(0, 1)]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.free_RVs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[obs ~ N(mu, 1)]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.observed_RVs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(-143.03962875)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.compile_logp()({\"mu\": 0})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's worth highlighting the design choice we made with `logp`.  As you can see above, `logp` is being called with arguments, so it's a method of the model instance. More precisely, it puts together a function based on the current state of the model -- or on the state given as argument to `logp` (see example below).\n",
-    "\n",
-    "For diverse reasons, we assume that a `Model` instance isn't static. If you need to use `logp` in an inner loop and it needs to be static, simply use something like `logp = model.logp`. Here is an example below -- note the caching effect and the speed up:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "83 ms ± 7.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "18 µs ± 276 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit model.compile_logp()({\"mu\": 0.1})\n",
-    "logp = model.compile_logp()\n",
-    "%timeit logp({\"mu\": 0.1})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Probability Distributions\n",
-    "\n",
-    "Every probabilistic program consists of observed and unobserved Random Variables (RVs). Observed RVs are defined via likelihood distributions, while unobserved RVs are defined via prior distributions. In the PyMC module, the structure for probability distributions looks like this:\n",
-    "\n",
-    "{ref}`pymc:api_distributions`\n",
-    "- {ref}`pymc:api_distributions_continuous`\n",
-    "- {ref}`pymc:api_distributions_discrete`\n",
-    "- {ref}`pymc:api_distributions_multivariate`\n",
-    "- {ref}`pymc:api_distributions_mixture`\n",
-    "- {ref}`pymc:api_distributions_timeseries`\n",
-    "- {ref}`pymc:api_distributions_censored`\n",
-    "- {ref}`pymc:api_distributions_simulator`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Unobserved Random Variables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Every unobserved RV has the following calling signature: name (str), parameter keyword arguments. Thus, a normal prior can be defined in a model context like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with pm.Model():\n",
-    "    x = pm.Normal(\"x\", mu=0, sigma=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As with the model, we can evaluate its logp:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(-0.91893853)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pm.logp(x, 0).eval()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Observed Random Variables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Observed RVs are defined just like unobserved RVs but require data to be passed into the `observed` keyword argument:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with pm.Model():\n",
-    "    obs = pm.Normal(\"x\", mu=0, sigma=1, observed=rng.standard_normal(100))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`observed` supports lists, `numpy.ndarray` and `pytensor` data structures."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Deterministic transforms"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "PyMC allows you to freely do algebra with RVs in all kinds of ways:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with pm.Model():\n",
-    "    x = pm.Normal(\"x\", mu=0, sigma=1)\n",
-    "    y = pm.Gamma(\"y\", alpha=1, beta=1)\n",
-    "    plus_2 = x + 2\n",
-    "    summed = x + y\n",
-    "    squared = x**2\n",
-    "    sined = pm.math.sin(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Though these transformations work seamlessly, their results are not stored automatically. Thus, if you want to keep track of a transformed variable, you have to use `pm.Deterministic`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with pm.Model():\n",
-    "    x = pm.Normal(\"x\", mu=0, sigma=1)\n",
-    "    plus_2 = pm.Deterministic(\"x plus 2\", x + 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that `plus_2` can be used in the identical way to above, we only tell PyMC to keep track of this RV for us."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lists of RVs / higher-dimensional RVs\n",
-    "\n",
-    "Above we have seen how to create scalar RVs. In many models, we want multiple RVs. Users will sometimes try to create lists of RVs, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with pm.Model():\n",
-    "    # bad:\n",
-    "    x = [pm.Normal(f\"x_{i}\", mu=0, sigma=1) for i in range(10)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This works, but it is slow and not recommended. Instead, we can use {ref}`coordinates <pymc:dimensionality>`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coords = {\"cities\": [\"Santiago\", \"Mumbai\", \"Tokyo\"]}\n",
-    "with pm.Model(coords=coords) as model:\n",
-    "    # good:\n",
-    "    x = pm.Normal(\"x\", mu=0, sigma=1, dims=\"cities\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`x` is now a array of length 3 and each of the 3 variables within this array is associated with a label.  This will make it very easy to distinguish the 3 different variables when we go to look at results. We can index into this array or do linear algebra operations on it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with model:\n",
-    "    y = x[0] * x[1]  # indexing is supported\n",
-    "    x.dot(x.T)  # linear algebra is supported"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Initialize Random Variables\n",
-    "\n",
-    "Though PyMC automatically initializes models, it is sometimes helpful to define initial values for RVs. This can be done via the `initval` kwarg:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'x': array([0., 0., 0., 0., 0.])}"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with pm.Model(coords={\"idx\": np.arange(5)}) as model:\n",
-    "    x = pm.Normal(\"x\", mu=0, sigma=1, dims=\"idx\")\n",
-    "\n",
-    "model.initial_point()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'x': array([-0.36012097, -0.16168135,  1.07485641, -0.08855632, -0.03857412])}"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with pm.Model(coords={\"idx\": np.arange(5)}) as model:\n",
-    "    x = pm.Normal(\"x\", mu=0, sigma=1, dims=\"idx\", initval=rng.standard_normal(5))\n",
-    "\n",
-    "model.initial_point()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This technique is sometimes useful when trying to identify problems with model specification or initialization."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Inference\n",
-    "\n",
-    "Once we have defined our model, we have to perform inference to approximate the posterior distribution. PyMC supports two broad classes of inference: sampling and variational inference.\n",
-    "\n",
-    "### 3.1 Sampling\n",
-    "\n",
-    "The main entry point to MCMC sampling algorithms is via the `pm.sample()` function. By default, this function tries to auto-assign the right sampler(s).  `pm.sample()` returns an `arviz.InferenceData` object. `InferenceData` objects can easily be saved/loaded from a file and can carry additional (meta)data such as date/version and posterior predictive samples. Take a look at the {ref}`ArviZ Quickstart <arviz:quickstart>` to learn more."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [mu]\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"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MmRgdlLjA6Sd"
+      },
+      "source": [
+        "(api_quickstart)=\n",
+        "# General API quickstart\n",
+        "\n",
+        ":::{post} May 31, 2022\n",
+        ":tags:\n",
+        ":category: beginner\n",
+        ":author: Christian Luhmann\n",
+        ":::"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "id": "PLHIHiMDA6Sd"
+      },
+      "outputs": [],
+      "source": [
+        "import arviz as az\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pymc as pm\n",
+        "import pytensor.tensor as pt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "id": "eQr5Cb5jA6Se"
+      },
+      "outputs": [],
+      "source": [
+        "RANDOM_SEED = 8927\n",
+        "rng = np.random.default_rng(RANDOM_SEED)\n",
+        "az.style.use(\"arviz-darkgrid\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "M8fy4K7cA6Se"
+      },
+      "source": [
+        "## 1. Model creation\n",
+        "\n",
+        "Models in PyMC are centered around the `Model` class. It has references to all random variables (RVs) and computes the model logp and its gradients. Usually, you would instantiate it as part of a `with` context:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "id": "Cdezyo45A6Se"
+      },
+      "outputs": [],
+      "source": [
+        "with pm.Model() as model:\n",
+        "    # Model definition\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GnZCxB7xA6Se"
+      },
+      "source": [
+        "We discuss RVs further below but let's create a simple model to explore the `Model` class."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "id": "S-jhHy1wA6Se"
+      },
+      "outputs": [],
+      "source": [
+        "with pm.Model() as model:\n",
+        "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
+        "    obs = pm.Normal(\"obs\", mu=mu, sigma=1, observed=rng.standard_normal(100))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "id": "NSOJ6l9EA6Sf",
+        "outputId": "0ba878db-1e56-478b-dfb8-63bbf703e6ec",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[mu, obs]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 49
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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 00:03<00:00 Sampling 2 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "model.basic_RVs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {
+        "id": "GAuR_YKzA6Sf",
+        "outputId": "31221b40-dc8f-4a19-ff3a-4b07c77981e4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[mu]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 50
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "model.free_RVs"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "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 4 seconds.\n"
-     ]
-    }
-   ],
-   "source": [
-    "with pm.Model() as model:\n",
-    "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
-    "    obs = pm.Normal(\"obs\", mu=mu, sigma=1, observed=rng.standard_normal(100))\n",
-    "\n",
-    "    idata = pm.sample(2000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, with model that exclusively contains continuous variables, PyMC assigns the NUTS sampler, which is very efficient even for complex models. PyMC also runs initial tuning to find good starting parameters for the sampler. Here we draw 2000 samples from the posterior in each chain and allow the sampler to adjust its parameters in an additional 1500 iterations.\n",
-    "\n",
-    "If not set via the `chains` kwarg, the number of chains is determined from the number of available CPU cores."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Frozen({'chain': 2, 'draw': 2000})"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idata.posterior.dims"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The tuning samples are discarded by default. With `discard_tuned_samples=False` they can be kept and end up in a separate group within the `InferenceData` object (i.e., `idata.warmup_posterior`).\n",
-    "\n",
-    "You can control how the chains are run in parallel using the `chains` and `cores` kwargs:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (6 chains in 4 jobs)\n",
-      "NUTS: [mu]\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"
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "id": "UIuiEDLXA6Sf",
+        "outputId": "4fcefcc5-d4a3-403a-c427-b60d5b53907a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[obs]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 51
+        }
       ],
-      "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 00:07<00:00 Sampling 6 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "model.observed_RVs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "rSduN4pyA6Sf",
+        "outputId": "76222020-16f9-4a0c-c427-45f95ead51b5",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(-143.03962875)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "model.compile_logp()({\"mu\": 0})"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 6 chains for 1_000 tune and 1_000 draw iterations (6_000 + 6_000 draws total) took 7 seconds.\n"
-     ]
-    }
-   ],
-   "source": [
-    "with pm.Model() as model:\n",
-    "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
-    "    obs = pm.Normal(\"obs\", mu=mu, sigma=1, observed=rng.standard_normal(100))\n",
-    "\n",
-    "    idata = pm.sample(cores=4, chains=6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(6, 1000)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idata.posterior[\"mu\"].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1000,)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# get values of a single chain\n",
-    "idata.posterior[\"mu\"].sel(chain=2).shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.2 Analyze sampling results\n",
-    "\n",
-    "The most common used plot to analyze sampling results is the so-called trace-plot:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [mu, sd]\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"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p5B_M6PJA6Sf"
+      },
+      "source": [
+        "It's worth highlighting the design choice we made with `logp`.  As you can see above, `logp` is being called with arguments, so it's a method of the model instance. More precisely, it puts together a function based on the current state of the model -- or on the state given as argument to `logp` (see example below).\n",
+        "\n",
+        "For diverse reasons, we assume that a `Model` instance isn't static. If you need to use `logp` in an inner loop and it needs to be static, simply use something like `logp = model.logp`. Here is an example below -- note the caching effect and the speed up:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {
+        "id": "eVRBf_1gA6Sf",
+        "outputId": "3b371788-17e0-4672-b114-43460526d4b3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "209 ms ± 85 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+            "32.3 µs ± 13.3 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\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 00:03<00:00 Sampling 2 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "%timeit model.compile_logp()({\"mu\": 0.1})\n",
+        "logp = model.compile_logp()\n",
+        "%timeit logp({\"mu\": 0.1})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tHqPk1IOA6Sf"
+      },
+      "source": [
+        "## 2. Probability Distributions\n",
+        "\n",
+        "Every probabilistic program consists of observed and unobserved Random Variables (RVs). Observed RVs are defined via likelihood distributions, while unobserved RVs are defined via prior distributions. In the PyMC module, the structure for probability distributions looks like this:\n",
+        "\n",
+        "{ref}`pymc:api_distributions`\n",
+        "- {ref}`pymc:api_distributions_continuous`\n",
+        "- {ref}`pymc:api_distributions_discrete`\n",
+        "- {ref}`pymc:api_distributions_multivariate`\n",
+        "- {ref}`pymc:api_distributions_mixture`\n",
+        "- {ref}`pymc:api_distributions_timeseries`\n",
+        "- {ref}`pymc:api_distributions_censored`\n",
+        "- {ref}`pymc:api_distributions_simulator`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zSNeAlCNA6Sg"
+      },
+      "source": [
+        "### Unobserved Random Variables"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dGB668vrA6Sg"
+      },
+      "source": [
+        "Every unobserved RV has the following calling signature: name (str), parameter keyword arguments. Thus, a normal prior can be defined in a model context like this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "id": "NLKSgGs8A6Sg"
+      },
+      "outputs": [],
+      "source": [
+        "with pm.Model():\n",
+        "    x = pm.Normal(\"x\", mu=0, sigma=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2tNc1qa-A6Sg"
+      },
+      "source": [
+        "As with the model, we can evaluate its logp:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "id": "5KbwxyHNA6Sg",
+        "outputId": "e096abd7-9170-41bc-d438-6e71267a71bc",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(-0.91893853)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 55
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "pm.logp(x, 0).eval()"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 4 seconds.\n"
-     ]
-    }
-   ],
-   "source": [
-    "with pm.Model() as model:\n",
-    "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
-    "    sd = pm.HalfNormal(\"sd\", sigma=1)\n",
-    "    obs = pm.Normal(\"obs\", mu=mu, sigma=sd, observed=rng.standard_normal(100))\n",
-    "\n",
-    "    idata = pm.sample()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x288 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_trace(idata);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another common metric to look at is the Gelman-Rubin statistic, or R-hat:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean</th>\n",
-       "      <th>sd</th>\n",
-       "      <th>hdi_3%</th>\n",
-       "      <th>hdi_97%</th>\n",
-       "      <th>mcse_mean</th>\n",
-       "      <th>mcse_sd</th>\n",
-       "      <th>ess_bulk</th>\n",
-       "      <th>ess_tail</th>\n",
-       "      <th>r_hat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>mu</th>\n",
-       "      <td>-0.019</td>\n",
-       "      <td>0.096</td>\n",
-       "      <td>-0.193</td>\n",
-       "      <td>0.163</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>1608.0</td>\n",
-       "      <td>1343.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sd</th>\n",
-       "      <td>0.967</td>\n",
-       "      <td>0.069</td>\n",
-       "      <td>0.835</td>\n",
-       "      <td>1.089</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>1836.0</td>\n",
-       "      <td>1406.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V8KxEXNKA6Sg"
+      },
+      "source": [
+        "### Observed Random Variables"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rTCYYS7LA6Sg"
+      },
+      "source": [
+        "Observed RVs are defined just like unobserved RVs but require data to be passed into the `observed` keyword argument:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {
+        "id": "O6_JNGMTA6Sg"
+      },
+      "outputs": [],
+      "source": [
+        "with pm.Model():\n",
+        "    obs = pm.Normal(\"x\", mu=0, sigma=1, observed=rng.standard_normal(100))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6mfLumKNA6Sg"
+      },
+      "source": [
+        "`observed` supports lists, `numpy.ndarray` and `pytensor` data structures."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WCZIky60A6Sg"
+      },
+      "source": [
+        "### Deterministic transforms"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LGM6UFckA6Sg"
+      },
+      "source": [
+        "PyMC allows you to freely do algebra with RVs in all kinds of ways:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "id": "twEmAvyiA6Sg"
+      },
+      "outputs": [],
+      "source": [
+        "with pm.Model():\n",
+        "    x = pm.Normal(\"x\", mu=0, sigma=1)\n",
+        "    y = pm.Gamma(\"y\", alpha=1, beta=1)\n",
+        "    plus_2 = x + 2\n",
+        "    summed = x + y\n",
+        "    squared = x**2\n",
+        "    sined = pm.math.sin(x)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m2PQSoiUA6Sg"
+      },
+      "source": [
+        "Though these transformations work seamlessly, their results are not stored automatically. Thus, if you want to keep track of a transformed variable, you have to use `pm.Deterministic`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "id": "09QISlW3A6Sg"
+      },
+      "outputs": [],
+      "source": [
+        "with pm.Model():\n",
+        "    x = pm.Normal(\"x\", mu=0, sigma=1)\n",
+        "    plus_2 = pm.Deterministic(\"x plus 2\", x + 2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1tw3hT11A6Sg"
+      },
+      "source": [
+        "Note that `plus_2` can be used in the identical way to above, we only tell PyMC to keep track of this RV for us."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z9t2MgPcA6Sg"
+      },
+      "source": [
+        "### Lists of RVs / higher-dimensional RVs\n",
+        "\n",
+        "Above we have seen how to create scalar RVs. In many models, we want multiple RVs. Users will sometimes try to create lists of RVs, like this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "id": "gbXJ6XOEA6Sh"
+      },
+      "outputs": [],
+      "source": [
+        "with pm.Model():\n",
+        "    # bad:\n",
+        "    x = [pm.Normal(f\"x_{i}\", mu=0, sigma=1) for i in range(10)]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iZRkOezSA6Sh"
+      },
+      "source": [
+        "This works, but it is slow and not recommended. Instead, we can use {ref}`coordinates <pymc:dimensionality>`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "id": "oWgmnhFvA6Sh"
+      },
+      "outputs": [],
+      "source": [
+        "coords = {\"cities\": [\"Santiago\", \"Mumbai\", \"Tokyo\"]}\n",
+        "with pm.Model(coords=coords) as model:\n",
+        "    # good:\n",
+        "    x = pm.Normal(\"x\", mu=0, sigma=1, dims=\"cities\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SLYGz65ZA6Sh"
+      },
+      "source": [
+        "`x` is now a array of length 3 and each of the 3 variables within this array is associated with a label.  This will make it very easy to distinguish the 3 different variables when we go to look at results. We can index into this array or do linear algebra operations on it:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "id": "gSL27wmqA6Sh"
+      },
+      "outputs": [],
+      "source": [
+        "with model:\n",
+        "    y = x[0] * x[1]  # indexing is supported\n",
+        "    x.dot(x.T)  # linear algebra is supported"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_wiWpfCEA6Sh"
+      },
+      "source": [
+        "### Initialize Random Variables\n",
+        "\n",
+        "Though PyMC automatically initializes models, it is sometimes helpful to define initial values for RVs. This can be done via the `initval` kwarg:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
+      "metadata": {
+        "id": "CBcxf2l3A6Sh",
+        "outputId": "9f0723e4-cc2f-4e28-c7fb-30537972592d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'x': array([0., 0., 0., 0., 0.])}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 62
+        }
       ],
-      "text/plain": [
-       "     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
-       "mu -0.019  0.096  -0.193    0.163      0.002    0.002    1608.0    1343.0   \n",
-       "sd  0.967  0.069   0.835    1.089      0.002    0.001    1836.0    1406.0   \n",
-       "\n",
-       "    r_hat  \n",
-       "mu    1.0  \n",
-       "sd    1.0  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "az.summary(idata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "R-hat is also presented as part of the `az.plot_forest`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 576x388.8 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_forest(idata, r_hat=True);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, for a plot of the posterior that is inspired by {cite:p}`kruschke2014doing`, you can use the:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 993.6x331.2 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_posterior(idata);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For high-dimensional models it becomes cumbersome to look at the traces for all parameters. When using `NUTS` we can look at the energy plot to assess problems of convergence:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [x]\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"
+      "source": [
+        "with pm.Model(coords={\"idx\": np.arange(5)}) as model:\n",
+        "    x = pm.Normal(\"x\", mu=0, sigma=1, dims=\"idx\")\n",
+        "\n",
+        "model.initial_point()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
+      "metadata": {
+        "id": "tjPPZgQMA6Sh",
+        "outputId": "8a274757-7b05-4f38-a3c3-3e899e852017",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'x': array([-0.36012097, -0.16168135,  1.07485641, -0.08855632, -0.03857412])}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 63
+        }
       ],
-      "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 00:04<00:00 Sampling 2 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "with pm.Model(coords={\"idx\": np.arange(5)}) as model:\n",
+        "    x = pm.Normal(\"x\", mu=0, sigma=1, dims=\"idx\", initval=rng.standard_normal(5))\n",
+        "\n",
+        "model.initial_point()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F7qdgmuoA6Sh"
+      },
+      "source": [
+        "This technique is sometimes useful when trying to identify problems with model specification or initialization."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kQv4ErDUA6Sl"
+      },
+      "source": [
+        "## 3. Inference\n",
+        "\n",
+        "Once we have defined our model, we have to perform inference to approximate the posterior distribution. PyMC supports two broad classes of inference: sampling and variational inference.\n",
+        "\n",
+        "### 3.1 Sampling\n",
+        "\n",
+        "The main entry point to MCMC sampling algorithms is via the `pm.sample()` function. By default, this function tries to auto-assign the right sampler(s).  `pm.sample()` returns an `arviz.InferenceData` object. `InferenceData` objects can easily be saved/loaded from a file and can carry additional (meta)data such as date/version and posterior predictive samples. Take a look at the {ref}`ArviZ Quickstart <arviz:quickstart>` to learn more."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "id": "lDCRIDAGA6Sm",
+        "outputId": "27ce3a4b-ef19-464d-9d39-d82df9139ae4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 133,
+          "referenced_widgets": [
+            "1678d49e24484f038d22dfcd6221025f",
+            "82e58cb2a4454cab861082056faefa5f"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1678d49e24484f038d22dfcd6221025f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "with pm.Model() as model:\n",
+        "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
+        "    obs = pm.Normal(\"obs\", mu=mu, sigma=1, observed=rng.standard_normal(100))\n",
+        "\n",
+        "    idata = pm.sample(2000)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 5 seconds.\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yfrpYofCA6Sm"
+      },
+      "source": [
+        "As you can see, with model that exclusively contains continuous variables, PyMC assigns the NUTS sampler, which is very efficient even for complex models. PyMC also runs initial tuning to find good starting parameters for the sampler. Here we draw 2000 samples from the posterior in each chain and allow the sampler to adjust its parameters in an additional 1500 iterations.\n",
+        "\n",
+        "If not set via the `chains` kwarg, the number of chains is determined from the number of available CPU cores."
+      ]
     },
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "id": "snsUfnlJA6Sm",
+        "outputId": "38b5b937-8b95-45fc-d1e0-fb728ab4925f",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "FrozenMappingWarningOnValuesAccess({'chain': 2, 'draw': 2000})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 65
+        }
+      ],
+      "source": [
+        "idata.posterior.dims"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with pm.Model(coords={\"idx\": np.arange(100)}) as model:\n",
-    "    x = pm.Normal(\"x\", mu=0, sigma=1, dims=\"idx\")\n",
-    "    idata = pm.sample()\n",
-    "\n",
-    "az.plot_energy(idata);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For more information on sampler stats and the energy plot, see {ref}`sampler_stats`. For more information on identifying sampling problems and what to do about them, see {ref}`diagnosing_with_divergences`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.3 Variational inference\n",
-    "\n",
-    "PyMC supports various Variational Inference techniques. While these methods are much faster, they are often also less accurate and can lead to biased inference. The main entry point is `pymc.fit()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "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"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRkPjCSOA6Sm"
+      },
+      "source": [
+        "The tuning samples are discarded by default. With `discard_tuned_samples=False` they can be kept and end up in a separate group within the `InferenceData` object (i.e., `idata.warmup_posterior`).\n",
+        "\n",
+        "You can control how the chains are run in parallel using the `chains` and `cores` kwargs:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "id": "I0M-ihiGA6Sm",
+        "outputId": "29f5539a-211f-4d9b-b005-f6fa8c349464",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 197,
+          "referenced_widgets": [
+            "401c78c98b014fd79d2656cdc3f73d3e",
+            "52390769fbfa42f0a83edc414f9a0206"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "401c78c98b014fd79d2656cdc3f73d3e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [10000/10000 00:00<00:00 Average Loss = 142.01]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "with pm.Model() as model:\n",
+        "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
+        "    obs = pm.Normal(\"obs\", mu=mu, sigma=1, observed=rng.standard_normal(100))\n",
+        "\n",
+        "    idata = pm.sample(cores=4, chains=6)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 67,
+      "metadata": {
+        "id": "lKhIp5hxA6Sm",
+        "outputId": "5f69050d-54bb-4a84-d6aa-ace6e1d81338",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(6, 1000)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 67
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "idata.posterior[\"mu\"].shape"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Finished [100%]: Average Loss = 142\n"
-     ]
-    }
-   ],
-   "source": [
-    "with pm.Model() as model:\n",
-    "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
-    "    sd = pm.HalfNormal(\"sd\", sigma=1)\n",
-    "    obs = pm.Normal(\"obs\", mu=mu, sigma=sd, observed=rng.standard_normal(100))\n",
-    "\n",
-    "    approx = pm.fit()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The returned `Approximation` object has various capabilities, like drawing samples from the approximated posterior, which we can analyse like a regular sampling run:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "arviz - WARNING - Shape validation failed: input_shape: (1, 1000), minimum_shape: (chains=2, draws=4)\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",
-       "\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>mean</th>\n",
-       "      <th>sd</th>\n",
-       "      <th>hdi_3%</th>\n",
-       "      <th>hdi_97%</th>\n",
-       "      <th>mcse_mean</th>\n",
-       "      <th>mcse_sd</th>\n",
-       "      <th>ess_bulk</th>\n",
-       "      <th>ess_tail</th>\n",
-       "      <th>r_hat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>mu</th>\n",
-       "      <td>-0.023</td>\n",
-       "      <td>0.169</td>\n",
-       "      <td>-0.338</td>\n",
-       "      <td>0.296</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>973.0</td>\n",
-       "      <td>880.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sd</th>\n",
-       "      <td>0.989</td>\n",
-       "      <td>0.158</td>\n",
-       "      <td>0.694</td>\n",
-       "      <td>1.262</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>972.0</td>\n",
-       "      <td>1026.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 68,
+      "metadata": {
+        "id": "Y7D0N0sOA6Sm",
+        "outputId": "d235a225-dce6-42ed-ee36-6815004e276b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1000,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 68
+        }
       ],
-      "text/plain": [
-       "     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
-       "mu -0.023  0.169  -0.338    0.296      0.005    0.004     973.0     880.0   \n",
-       "sd  0.989  0.158   0.694    1.262      0.005    0.004     972.0    1026.0   \n",
-       "\n",
-       "    r_hat  \n",
-       "mu    NaN  \n",
-       "sd    NaN  "
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idata = approx.sample(1000)\n",
-    "az.summary(idata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `variational` submodule offers a lot of flexibility in which VI to use and follows an object oriented design. For example, full-rank ADVI estimates a full covariance matrix:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "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"
+      "source": [
+        "# get values of a single chain\n",
+        "idata.posterior[\"mu\"].sel(chain=2).shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iR8987I4A6Sm"
+      },
+      "source": [
+        "### 3.2 Analyze sampling results\n",
+        "\n",
+        "The most common used plot to analyze sampling results is the so-called trace-plot:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 69,
+      "metadata": {
+        "id": "qzipPVYdA6Sn",
+        "outputId": "d76cac4f-d8ff-4820-b317-79a8d7317595",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 133,
+          "referenced_widgets": [
+            "d0b681855a864b2689d864cff7cf9c24",
+            "e6c97e7d9cea4703b5573c3f4dc8d1d5"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d0b681855a864b2689d864cff7cf9c24"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [10000/10000 00:03<00:00 Average Loss = 0.013113]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "with pm.Model() as model:\n",
+        "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
+        "    sd = pm.HalfNormal(\"sd\", sigma=1)\n",
+        "    obs = pm.Normal(\"obs\", mu=mu, sigma=sd, observed=rng.standard_normal(100))\n",
+        "\n",
+        "    idata = pm.sample()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "id": "quKDc3uAA6Sn",
+        "outputId": "65dd5943-fe5d-446a-d68c-a13ab7241f99",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 448
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 4 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "az.plot_trace(idata);"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Finished [100%]: Average Loss = 0.012772\n"
-     ]
-    }
-   ],
-   "source": [
-    "mu = pm.floatX([0.0, 0.0])\n",
-    "cov = pm.floatX([[1, 0.5], [0.5, 1.0]])\n",
-    "with pm.Model(coords={\"idx\": np.arange(2)}) as model:\n",
-    "    pm.MvNormal(\"x\", mu=mu, cov=cov, dims=\"idx\")\n",
-    "    approx = pm.fit(method=\"fullrank_advi\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An equivalent expression using the object-oriented interface is:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "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"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aRSIqnBOA6Sn"
+      },
+      "source": [
+        "Another common metric to look at is the Gelman-Rubin statistic, or R-hat:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 71,
+      "metadata": {
+        "id": "kqlPaSs_A6Sn",
+        "outputId": "5a19ae53-7ee2-4a35-9cfe-38a35cb5a629",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 132
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
+              "mu -0.019  0.095  -0.189    0.166      0.002    0.002    1753.0    1064.0   \n",
+              "sd  0.967  0.072   0.831    1.099      0.002    0.002    1551.0    1387.0   \n",
+              "\n",
+              "    r_hat  \n",
+              "mu    1.0  \n",
+              "sd    1.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3bd9ca73-5225-40a2-a84a-800dc072ecc9\" class=\"colab-df-container\">\n",
+              "    <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>mean</th>\n",
+              "      <th>sd</th>\n",
+              "      <th>hdi_3%</th>\n",
+              "      <th>hdi_97%</th>\n",
+              "      <th>mcse_mean</th>\n",
+              "      <th>mcse_sd</th>\n",
+              "      <th>ess_bulk</th>\n",
+              "      <th>ess_tail</th>\n",
+              "      <th>r_hat</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>mu</th>\n",
+              "      <td>-0.019</td>\n",
+              "      <td>0.095</td>\n",
+              "      <td>-0.189</td>\n",
+              "      <td>0.166</td>\n",
+              "      <td>0.002</td>\n",
+              "      <td>0.002</td>\n",
+              "      <td>1753.0</td>\n",
+              "      <td>1064.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sd</th>\n",
+              "      <td>0.967</td>\n",
+              "      <td>0.072</td>\n",
+              "      <td>0.831</td>\n",
+              "      <td>1.099</td>\n",
+              "      <td>0.002</td>\n",
+              "      <td>0.002</td>\n",
+              "      <td>1551.0</td>\n",
+              "      <td>1387.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3bd9ca73-5225-40a2-a84a-800dc072ecc9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3bd9ca73-5225-40a2-a84a-800dc072ecc9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3bd9ca73-5225-40a2-a84a-800dc072ecc9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-4bdf817e-442f-4df7-a56c-b86953342ada\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4bdf817e-442f-4df7-a56c-b86953342ada')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-4bdf817e-442f-4df7-a56c-b86953342ada button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"az\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6972072862499359,\n        \"min\": -0.019,\n        \"max\": 0.967,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.967,\n          -0.019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016263455967290598,\n        \"min\": 0.072,\n        \"max\": 0.095,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.072,\n          0.095\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hdi_3%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7212489168102785,\n        \"min\": -0.189,\n        \"max\": 0.831,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.831,\n          -0.189\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hdi_97%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6597306268470488,\n        \"min\": 0.166,\n        \"max\": 1.099,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.099,\n          0.166\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcse_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.002,\n        \"max\": 0.002,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcse_sd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.002,\n        \"max\": 0.002,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ess_bulk\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142.8355697996826,\n        \"min\": 1551.0,\n        \"max\": 1753.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1551.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ess_tail\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 228.39549032325485,\n        \"min\": 1064.0,\n        \"max\": 1387.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1387.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"r_hat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 71
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [10000/10000 00:03<00:00 Average Loss = 0.020591]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "az.summary(idata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-TbJ7zJaA6Sn"
+      },
+      "source": [
+        "R-hat is also presented as part of the `az.plot_forest`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "id": "cqR274S5A6Sn",
+        "outputId": "09d0358f-3558-45b0-e59f-0cd0afbeeafc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 588
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x540 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "az.plot_forest(idata, r_hat=True);"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Finished [100%]: Average Loss = 0.020531\n"
-     ]
-    }
-   ],
-   "source": [
-    "with pm.Model(coords={\"idx\": np.arange(2)}) as model:\n",
-    "    pm.MvNormal(\"x\", mu=mu, cov=cov, dims=\"idx\")\n",
-    "    approx = pm.FullRankADVI().fit()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "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"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FTddbIgZA6Sn"
+      },
+      "source": [
+        "Finally, for a plot of the posterior that is inspired by {cite:p}`kruschke2014doing`, you can use the:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "id": "VYTtIvZAA6Sn",
+        "outputId": "015bc260-8484-4fb4-a639-b418f49c4194",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 490
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1656x552 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [10000/10000 00:03<00:00 Average Loss = 0.014234]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "az.plot_posterior(idata);"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6CR0tqhEA6Sn"
+      },
+      "source": [
+        "For high-dimensional models it becomes cumbersome to look at the traces for all parameters. When using `NUTS` we can look at the energy plot to assess problems of convergence:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 74,
+      "metadata": {
+        "id": "WGGGa8kLA6Sn",
+        "outputId": "9192d00d-35a6-4a36-f296-e45f7d2d5c6a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 624,
+          "referenced_widgets": [
+            "5da052e8f9164784a75050f10747c08a",
+            "7e636c641bd14c5fbb8655481177ee9d"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5da052e8f9164784a75050f10747c08a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "with pm.Model(coords={\"idx\": np.arange(100)}) as model:\n",
+        "    x = pm.Normal(\"x\", mu=0, sigma=1, dims=\"idx\")\n",
+        "    idata = pm.sample()\n",
+        "\n",
+        "az.plot_energy(idata);"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Finished [100%]: Average Loss = 0.014143\n"
-     ]
-    }
-   ],
-   "source": [
-    "with pm.Model(coords={\"idx\": np.arange(2)}) as model:\n",
-    "    pm.MvNormal(\"x\", mu=mu, cov=cov, dims=\"idx\")\n",
-    "    approx = pm.FullRankADVI().fit()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "idata = approx.sample(10000)\n",
-    "az.plot_pair(idata, var_names=\"x\", coords={\"idx\": [0, 1]});"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Stein Variational Gradient Descent (SVGD) uses particles to estimate the posterior:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "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"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uSPGF3YyA6Sn"
+      },
+      "source": [
+        "For more information on sampler stats and the energy plot, see {ref}`sampler_stats`. For more information on identifying sampling problems and what to do about them, see {ref}`diagnosing_with_divergences`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FEH2_9JtA6Sn"
+      },
+      "source": [
+        "### 3.3 Variational inference\n",
+        "\n",
+        "PyMC supports various Variational Inference techniques. While these methods are much faster, they are often also less accurate and can lead to biased inference. The main entry point is `pymc.fit()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {
+        "id": "9iAiegfqA6So",
+        "outputId": "df37a4e7-32e9-479f-cf75-3643fc434422",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53,
+          "referenced_widgets": [
+            "85eca283c2a146cc83e66e9081292c61",
+            "f204532cf22f4b56b93d959898bef98d"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "85eca283c2a146cc83e66e9081292c61"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [10000/10000 01:20<00:00]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "with pm.Model() as model:\n",
+        "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
+        "    sd = pm.HalfNormal(\"sd\", sigma=1)\n",
+        "    obs = pm.Normal(\"obs\", mu=mu, sigma=sd, observed=rng.standard_normal(100))\n",
+        "\n",
+        "    approx = pm.fit()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w6gc6GZKA6So"
+      },
+      "source": [
+        "The returned `Approximation` object has various capabilities, like drawing samples from the approximated posterior, which we can analyse like a regular sampling run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "id": "U24qs7UrA6So",
+        "outputId": "d74aa724-7d9d-4122-c7db-4c41c2ea975d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 149
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Shape validation failed: input_shape: (1, 1000), minimum_shape: (chains=2, draws=4)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
+              "mu -0.035  0.171  -0.326    0.289      0.005    0.004    1026.0    1016.0   \n",
+              "sd  0.993  0.143   0.716    1.237      0.004    0.003    1092.0     990.0   \n",
+              "\n",
+              "    r_hat  \n",
+              "mu    NaN  \n",
+              "sd    NaN  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9e1ee0ee-d759-4e0f-80b4-a97d31ce7e9b\" class=\"colab-df-container\">\n",
+              "    <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>mean</th>\n",
+              "      <th>sd</th>\n",
+              "      <th>hdi_3%</th>\n",
+              "      <th>hdi_97%</th>\n",
+              "      <th>mcse_mean</th>\n",
+              "      <th>mcse_sd</th>\n",
+              "      <th>ess_bulk</th>\n",
+              "      <th>ess_tail</th>\n",
+              "      <th>r_hat</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>mu</th>\n",
+              "      <td>-0.035</td>\n",
+              "      <td>0.171</td>\n",
+              "      <td>-0.326</td>\n",
+              "      <td>0.289</td>\n",
+              "      <td>0.005</td>\n",
+              "      <td>0.004</td>\n",
+              "      <td>1026.0</td>\n",
+              "      <td>1016.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sd</th>\n",
+              "      <td>0.993</td>\n",
+              "      <td>0.143</td>\n",
+              "      <td>0.716</td>\n",
+              "      <td>1.237</td>\n",
+              "      <td>0.004</td>\n",
+              "      <td>0.003</td>\n",
+              "      <td>1092.0</td>\n",
+              "      <td>990.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e1ee0ee-d759-4e0f-80b4-a97d31ce7e9b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9e1ee0ee-d759-4e0f-80b4-a97d31ce7e9b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9e1ee0ee-d759-4e0f-80b4-a97d31ce7e9b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-ca7f04bd-4ed3-4d55-8205-06915550a86d\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ca7f04bd-4ed3-4d55-8205-06915550a86d')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-ca7f04bd-4ed3-4d55-8205-06915550a86d button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"az\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7269057710597708,\n        \"min\": -0.035,\n        \"max\": 0.993,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.993,\n          -0.035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.019798989873223347,\n        \"min\": 0.143,\n        \"max\": 0.171,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.143,\n          0.171\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hdi_3%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7368052659963826,\n        \"min\": -0.326,\n        \"max\": 0.716,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.716,\n          -0.326\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hdi_97%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6703372285648471,\n        \"min\": 0.289,\n        \"max\": 1.237,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.237,\n          0.289\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcse_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0007071067811865476,\n        \"min\": 0.004,\n        \"max\": 0.005,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.004,\n          0.005\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcse_sd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0007071067811865475,\n        \"min\": 0.003,\n        \"max\": 0.004,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.003,\n          0.004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ess_bulk\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.66904755831214,\n        \"min\": 1026.0,\n        \"max\": 1092.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1092.0,\n          1026.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ess_tail\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.384776310850235,\n        \"min\": 990.0,\n        \"max\": 1016.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          990.0,\n          1016.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"r_hat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 76
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "idata = approx.sample(1000)\n",
+        "az.summary(idata)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "w = pm.floatX([0.2, 0.8])\n",
-    "mu = pm.floatX([-0.3, 0.5])\n",
-    "sd = pm.floatX([0.1, 0.1])\n",
-    "with pm.Model() as model:\n",
-    "    pm.NormalMixture(\"x\", w=w, mu=mu, sigma=sd)\n",
-    "    approx = pm.fit(method=pm.SVGD(n_particles=200, jitter=1.0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with pm.Model() as model:\n",
-    "    pm.NormalMixture(\"x\", w=[0.2, 0.8], mu=[-0.3, 0.5], sigma=[0.1, 0.1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "idata = approx.sample(10000)\n",
-    "az.plot_dist(idata.posterior[\"x\"]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For more information on variational inference, see {ref}`variational_inference`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Posterior Predictive Sampling\n",
-    "\n",
-    "The `sample_posterior_predictive()` function performs prediction on hold-out data and posterior predictive checks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [mu, sd]\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"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vbPh1PXwA6So"
+      },
+      "source": [
+        "The `variational` submodule offers a lot of flexibility in which VI to use and follows an object oriented design. For example, full-rank ADVI estimates a full covariance matrix:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 77,
+      "metadata": {
+        "id": "92P1VVYjA6So",
+        "outputId": "913e385c-dd89-4395-bebd-a70f0b8856c8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53,
+          "referenced_widgets": [
+            "85469b8bdf4d4d86a68a629504bc069b",
+            "f3749d99163540ad9d7dcfc99c180f65"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "85469b8bdf4d4d86a68a629504bc069b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "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 00:03<00:00 Sampling 2 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "mu = pm.floatX([0.0, 0.0])\n",
+        "cov = pm.floatX([[1, 0.5], [0.5, 1.0]])\n",
+        "with pm.Model(coords={\"idx\": np.arange(2)}) as model:\n",
+        "    pm.MvNormal(\"x\", mu=mu, cov=cov, dims=\"idx\")\n",
+        "    approx = pm.fit(method=\"fullrank_advi\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Or6eJdiuA6So"
+      },
+      "source": [
+        "An equivalent expression using the object-oriented interface is:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 78,
+      "metadata": {
+        "id": "O7-qSdTxA6So",
+        "outputId": "0a178dcc-ab2f-432c-d363-dcee4f20d2b7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53,
+          "referenced_widgets": [
+            "fa2f555f5e5447ada62defd5f0a6de35",
+            "d13b067159974f47b4cc7129cb95af81"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fa2f555f5e5447ada62defd5f0a6de35"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "with pm.Model(coords={\"idx\": np.arange(2)}) as model:\n",
+        "    pm.MvNormal(\"x\", mu=mu, cov=cov, dims=\"idx\")\n",
+        "    approx = pm.FullRankADVI().fit()"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 4 seconds.\n"
-     ]
-    }
-   ],
-   "source": [
-    "data = rng.standard_normal(100)\n",
-    "with pm.Model() as model:\n",
-    "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
-    "    sd = pm.HalfNormal(\"sd\", sigma=1)\n",
-    "    obs = pm.Normal(\"obs\", mu=mu, sigma=sd, observed=data)\n",
-    "\n",
-    "    idata = pm.sample()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "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"
+      "cell_type": "code",
+      "execution_count": 79,
+      "metadata": {
+        "id": "7T5vQiRHA6So",
+        "outputId": "94e0d82a-9f8e-44f7-e958-e6346b410429",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53,
+          "referenced_widgets": [
+            "4c8a3c91387a408399cd315eb2a18835",
+            "1245a827320d4ad78332987695a1c179"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4c8a3c91387a408399cd315eb2a18835"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='2000' class='' max='2000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [2000/2000 00:00<00:00]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "with pm.Model(coords={\"idx\": np.arange(2)}) as model:\n",
+        "    pm.MvNormal(\"x\", mu=mu, cov=cov, dims=\"idx\")\n",
+        "    approx = pm.FullRankADVI().fit()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 80,
+      "metadata": {
+        "id": "bESl0ml_A6So",
+        "outputId": "3a77fe2a-3010-4d33-883d-8799a7b5a5b3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 545
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "plt.figure()\n",
+        "idata = approx.sample(10000)\n",
+        "az.plot_pair(idata, var_names=\"x\", coords={\"idx\": [0, 1]});"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with model:\n",
-    "    idata.extend(pm.sample_posterior_predictive(idata))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/xian/anaconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
-      "  fig.canvas.print_figure(bytes_io, **kw)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "az.plot_ppc(idata, ax=ax)\n",
-    "ax.axvline(data.mean(), ls=\"--\", color=\"r\", label=\"True mean\")\n",
-    "ax.legend(fontsize=10);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4.1 Predicting on hold-out data\n",
-    "\n",
-    "In many cases you want to predict on unseen / hold-out data. This is especially relevant in Probabilistic Machine Learning and Bayesian Deep Learning. PyMC includes a `pm.MutableData` container to help with such uses. It is a wrapper around a `pytensor.shared` variable and allows the values of the data to be changed later. Otherwise, `pm.MutableData` objects can be used just like any other numpy array or tensor.\n",
-    "\n",
-    "This distinction is significant since internally all models in PyMC are giant symbolic expressions. When you pass raw data directly into a model, you are giving PyTensor permission to treat this data as a constant and optimize it away if doing so makes sense. If you need to change this data later you may not have any way to point at it within the larger symbolic expression. Using `pm.MutableData` offers a way to point to a specific place in the symbolic expression and change what is there."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [x]\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"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zm6UecmGA6So"
+      },
+      "source": [
+        "Stein Variational Gradient Descent (SVGD) uses particles to estimate the posterior:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 81,
+      "metadata": {
+        "id": "t8-BKgUcA6So",
+        "outputId": "5de2d94e-6cf7-4e45-9165-67d0feb5e1ac",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53,
+          "referenced_widgets": [
+            "b6697919f8304d57aeac480d6d852871",
+            "7da4224189fe421bb4038c0959f502a9"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b6697919f8304d57aeac480d6d852871"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "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 00:03<00:00 Sampling 2 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "w = pm.floatX([0.2, 0.8])\n",
+        "mu = pm.floatX([-0.3, 0.5])\n",
+        "sd = pm.floatX([0.1, 0.1])\n",
+        "with pm.Model() as model:\n",
+        "    pm.NormalMixture(\"x\", w=w, mu=mu, sigma=sd)\n",
+        "    approx = pm.fit(method=pm.SVGD(n_particles=200, jitter=1.0))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 82,
+      "metadata": {
+        "id": "k0saBTsZA6Sp"
+      },
+      "outputs": [],
+      "source": [
+        "with pm.Model() as model:\n",
+        "    pm.NormalMixture(\"x\", w=[0.2, 0.8], mu=[-0.3, 0.5], sigma=[0.1, 0.1])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 83,
+      "metadata": {
+        "id": "qclGfPzNA6Sp",
+        "outputId": "b552516a-132f-4134-a710-225fca043f4c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 528
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "plt.figure()\n",
+        "idata = approx.sample(10000)\n",
+        "az.plot_dist(idata.posterior[\"x\"]);"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 3 seconds.\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = rng.standard_normal(100)\n",
-    "y = x > 0\n",
-    "\n",
-    "coords = {\"idx\": np.arange(100)}\n",
-    "with pm.Model() as model:\n",
-    "    # create shared variables that can be changed later on\n",
-    "    x_obs = pm.MutableData(\"x_obs\", x, dims=\"idx\")\n",
-    "    y_obs = pm.MutableData(\"y_obs\", y, dims=\"idx\")\n",
-    "\n",
-    "    coeff = pm.Normal(\"x\", mu=0, sigma=1)\n",
-    "    logistic = pm.math.sigmoid(coeff * x_obs)\n",
-    "    pm.Bernoulli(\"obs\", p=logistic, observed=y_obs, dims=\"idx\")\n",
-    "    idata = pm.sample()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now assume we want to predict on unseen data. For this we have to change the values of `x_obs` and `y_obs`. Theoretically we don't need to set `y_obs` as we want to predict it but it has to match the shape of `x_obs`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "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"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "17fNWr1JA6Sp"
+      },
+      "source": [
+        "For more information on variational inference, see {ref}`variational_inference`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8quFcQVRA6Sp"
+      },
+      "source": [
+        "## 4. Posterior Predictive Sampling\n",
+        "\n",
+        "The `sample_posterior_predictive()` function performs prediction on hold-out data and posterior predictive checks."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 84,
+      "metadata": {
+        "id": "V1db4J35A6Sp",
+        "outputId": "ef0dc4d4-9ed4-4ef6-ee04-ca7d798b0442",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 133,
+          "referenced_widgets": [
+            "0c957dfdcd8c43e9990a0bf9ea3eb45f",
+            "40ef9fcda6b6494eb9bcc269598b6c68"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0c957dfdcd8c43e9990a0bf9ea3eb45f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='2000' class='' max='2000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [2000/2000 00:00<00:00]\n",
-       "    </div>\n",
-       "    "
+      "source": [
+        "data = rng.standard_normal(100)\n",
+        "with pm.Model() as model:\n",
+        "    mu = pm.Normal(\"mu\", mu=0, sigma=1)\n",
+        "    sd = pm.HalfNormal(\"sd\", sigma=1)\n",
+        "    obs = pm.Normal(\"obs\", mu=mu, sigma=sd, observed=data)\n",
+        "\n",
+        "    idata = pm.sample()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 85,
+      "metadata": {
+        "id": "lT8XWIi0A6Sp",
+        "outputId": "8afc16d9-f5a6-475d-98c5-ae4705487584",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53,
+          "referenced_widgets": [
+            "c0baa173f2b247a9a3605a9a5a155d8a",
+            "6e4f4d7d80114d2a9c3e72a7d0651a78"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c0baa173f2b247a9a3605a9a5a155d8a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "with model:\n",
+        "    idata.extend(pm.sample_posterior_predictive(idata))"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with model:\n",
-    "    # change the value and shape of the data\n",
-    "    pm.set_data(\n",
-    "        {\n",
-    "            \"x_obs\": [-1, 0, 1.0],\n",
-    "            # use dummy values with the same shape:\n",
-    "            \"y_obs\": [0, 0, 0],\n",
-    "        },\n",
-    "        coords={\"idx\": [1001, 1002, 1003]},\n",
-    "    )\n",
-    "\n",
-    "    idata.extend(pm.sample_posterior_predictive(idata))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "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",
-       " *\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.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-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",
-       "  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",
-       "  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-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-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",
-       "  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.DataArray &#x27;obs&#x27; (idx: 3)&gt;\n",
-       "array([0.0215, 0.488 , 0.982 ])\n",
-       "Coordinates:\n",
-       "  * idx      (idx) int64 1001 1002 1003</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'obs'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>idx</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a224efb2-191d-4913-882a-73a4b16b6a81' class='xr-array-in' type='checkbox' checked><label for='section-a224efb2-191d-4913-882a-73a4b16b6a81' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0215 0.488 0.982</span></div><div class='xr-array-data'><pre>array([0.0215, 0.488 , 0.982 ])</pre></div></div></li><li class='xr-section-item'><input id='section-5292152c-4e8e-4ea7-937d-5a6669f26c6d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5292152c-4e8e-4ea7-937d-5a6669f26c6d' 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'>idx</span></div><div class='xr-var-dims'>(idx)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1001 1002 1003</div><input id='attrs-e6d02078-729f-4be2-81d5-3f9046535938' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6d02078-729f-4be2-81d5-3f9046535938' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22b47c07-1831-4dc0-bc3f-575cb4e2216c' class='xr-var-data-in' type='checkbox'><label for='data-22b47c07-1831-4dc0-bc3f-575cb4e2216c' 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([1001, 1002, 1003])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9fdcb243-f6ab-4a28-80ca-4fed5c87812f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9fdcb243-f6ab-4a28-80ca-4fed5c87812f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 86,
+      "metadata": {
+        "id": "6vQWPkKTA6Sp",
+        "outputId": "d8a9e97c-cc3b-47ee-fdbb-6326094cb93c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 528
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots()\n",
+        "az.plot_ppc(idata, ax=ax)\n",
+        "ax.axvline(data.mean(), ls=\"--\", color=\"r\", label=\"True mean\")\n",
+        "ax.legend(fontsize=10);"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c64FdGkpA6Sp"
+      },
+      "source": [
+        "## 4.1 Predicting on hold-out data\n",
+        "\n",
+        "In many cases you want to predict on unseen / hold-out data. This is especially relevant in Probabilistic Machine Learning and Bayesian Deep Learning. PyMC includes a `pm.MutableData` container to help with such uses. It is a wrapper around a `pytensor.shared` variable and allows the values of the data to be changed later. Otherwise, `pm.MutableData` objects can be used just like any other numpy array or tensor.\n",
+        "\n",
+        "This distinction is significant since internally all models in PyMC are giant symbolic expressions. When you pass raw data directly into a model, you are giving PyTensor permission to treat this data as a constant and optimize it away if doing so makes sense. If you need to change this data later you may not have any way to point at it within the larger symbolic expression. Using `pm.MutableData` offers a way to point to a specific place in the symbolic expression and change what is there."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 88,
+      "metadata": {
+        "id": "9SflE1FbA6Sp",
+        "outputId": "15e828a5-f0f6-4857-b276-48d334269423",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 133,
+          "referenced_widgets": [
+            "f6147c1b4e6148ad9d9baf0cd6cd1f19",
+            "248e5af018d24a7abb142d957d7e04ba"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f6147c1b4e6148ad9d9baf0cd6cd1f19"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "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"
+            ]
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<xarray.DataArray 'obs' (idx: 3)>\n",
-       "array([0.0215, 0.488 , 0.982 ])\n",
-       "Coordinates:\n",
-       "  * idx      (idx) int64 1001 1002 1003"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
+      "source": [
+        "x = rng.standard_normal(100)\n",
+        "y = x > 0\n",
+        "\n",
+        "coords = {\"idx\": np.arange(100)}\n",
+        "with pm.Model() as model:\n",
+        "    # create shared variables that can be changed later on\n",
+        "    x_obs = pm.Data(\"x_obs\", x, dims=\"idx\")\n",
+        "    y_obs = pm.Data(\"y_obs\", y, dims=\"idx\")\n",
+        "\n",
+        "\n",
+        "    coeff = pm.Normal(\"x\", mu=0, sigma=1)\n",
+        "    logistic = pm.math.sigmoid(coeff * x_obs)\n",
+        "    pm.Bernoulli(\"obs\", p=logistic, observed=y_obs, dims=\"idx\")\n",
+        "    idata = pm.sample()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7VIsw8QnA6Sp"
+      },
+      "source": [
+        "Now assume we want to predict on unseen data. For this we have to change the values of `x_obs` and `y_obs`. Theoretically we don't need to set `y_obs` as we want to predict it but it has to match the shape of `x_obs`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zZ5JhWp8A6Sp"
+      },
+      "outputs": [],
+      "source": [
+        "with model:\n",
+        "    # change the value and shape of the data\n",
+        "    pm.set_data(\n",
+        "        {\n",
+        "            \"x_obs\": [-1, 0, 1.0],\n",
+        "            # use dummy values with the same shape:\n",
+        "            \"y_obs\": [0, 0, 0],\n",
+        "        },\n",
+        "        coords={\"idx\": [1001, 1002, 1003]},\n",
+        "    )\n",
+        "\n",
+        "    idata.extend(pm.sample_posterior_predictive(idata))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9PhTHtqyA6Sp"
+      },
+      "outputs": [],
+      "source": [
+        "idata.posterior_predictive[\"obs\"].mean(dim=[\"draw\", \"chain\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0vFuq_9AA6Sp"
+      },
+      "source": [
+        "## References\n",
+        "\n",
+        ":::{bibliography}\n",
+        ":filter: docname in docnames\n",
+        ":::"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ALP_vZDhA6Sp"
+      },
+      "outputs": [],
+      "source": [
+        "%load_ext watermark\n",
+        "%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UIESZyJCA6Sq"
+      },
+      "source": [
+        ":::{include} ../page_footer.md\n",
+        ":::"
+      ]
     }
-   ],
-   "source": [
-    "idata.posterior_predictive[\"obs\"].mean(dim=[\"draw\", \"chain\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "\n",
-    ":::{bibliography}\n",
-    ":filter: docname in docnames\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Last updated: Fri Jun 03 2022\n",
-      "\n",
-      "Python implementation: CPython\n",
-      "Python version       : 3.9.13\n",
-      "IPython version      : 8.4.0\n",
-      "\n",
-      "pytensor: 2.6.2\n",
-      "aeppl : 0.0.31\n",
-      "xarray: 2022.3.0\n",
-      "\n",
-      "arviz     : 0.12.1\n",
-      "numpy     : 1.22.4\n",
-      "pymc      : 4.0.0b6\n",
-      "pytensor    : 2.6.2\n",
-      "matplotlib: 3.5.2\n",
-      "\n",
-      "Watermark: 2.3.1\n",
-      "\n"
-     ]
+  ],
+  "metadata": {
+    "anaconda-cloud": {},
+    "jupytext": {
+      "notebook_metadata_filter": "substitutions"
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.13"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "1678d49e24484f038d22dfcd6221025f": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_82e58cb2a4454cab861082056faefa5f",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "                                                                                                                   \n \u001b[1m \u001b[0m\u001b[1mProgress                \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDraws\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDivergences\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mStep size\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mGrad evals\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mSampling Speed \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mRemaining\u001b[0m\u001b[1m \u001b[0m \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   3000    0             2.060       1            1896.36 draws/s   0:00:01   0:00:00    \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   3000    0             1.330       3            1118.70 draws/s   0:00:02   0:00:00    \n                                                                                                                   \n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">                                                                                                                   \n <span style=\"font-weight: bold\"> Progress                 </span> <span style=\"font-weight: bold\"> Draws </span> <span style=\"font-weight: bold\"> Divergences </span> <span style=\"font-weight: bold\"> Step size </span> <span style=\"font-weight: bold\"> Grad evals </span> <span style=\"font-weight: bold\"> Sampling Speed  </span> <span style=\"font-weight: bold\"> Elapsed </span> <span style=\"font-weight: bold\"> Remaining </span> \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━</span>   3000    0             2.060       1            1896.36 draws/s   0:00:01   0:00:00    \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━</span>   3000    0             1.330       3            1118.70 draws/s   0:00:02   0:00:00    \n                                                                                                                   \n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "82e58cb2a4454cab861082056faefa5f": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "401c78c98b014fd79d2656cdc3f73d3e": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_52390769fbfa42f0a83edc414f9a0206",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "                                                                                                                   \n \u001b[1m \u001b[0m\u001b[1mProgress                 \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDraws\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDivergences\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mStep size\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mGrad evals\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mSampling Speed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mRemaining\u001b[0m\u001b[1m \u001b[0m \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             1.045       3            448.18 draws/s   0:00:04   0:00:00    \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             1.573       1            418.85 draws/s   0:00:04   0:00:00    \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             1.480       3            373.50 draws/s   0:00:05   0:00:00    \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             1.468       3            348.68 draws/s   0:00:05   0:00:00    \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             1.229       3            274.38 draws/s   0:00:07   0:00:00    \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             2.473       1            258.23 draws/s   0:00:07   0:00:00    \n                                                                                                                   \n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">                                                                                                                   \n <span style=\"font-weight: bold\"> Progress                  </span> <span style=\"font-weight: bold\"> Draws </span> <span style=\"font-weight: bold\"> Divergences </span> <span style=\"font-weight: bold\"> Step size </span> <span style=\"font-weight: bold\"> Grad evals </span> <span style=\"font-weight: bold\"> Sampling Speed </span> <span style=\"font-weight: bold\"> Elapsed </span> <span style=\"font-weight: bold\"> Remaining </span> \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             1.045       3            448.18 draws/s   0:00:04   0:00:00    \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             1.573       1            418.85 draws/s   0:00:04   0:00:00    \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             1.480       3            373.50 draws/s   0:00:05   0:00:00    \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             1.468       3            348.68 draws/s   0:00:05   0:00:00    \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             1.229       3            274.38 draws/s   0:00:07   0:00:00    \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             2.473       1            258.23 draws/s   0:00:07   0:00:00    \n                                                                                                                   \n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "52390769fbfa42f0a83edc414f9a0206": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "d0b681855a864b2689d864cff7cf9c24": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_e6c97e7d9cea4703b5573c3f4dc8d1d5",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "                                                                                                                   \n \u001b[1m \u001b[0m\u001b[1mProgress                \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDraws\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDivergences\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mStep size\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mGrad evals\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mSampling Speed \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mRemaining\u001b[0m\u001b[1m \u001b[0m \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             1.257       3            1323.04 draws/s   0:00:01   0:00:00    \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             0.840       3            745.48 draws/s    0:00:02   0:00:00    \n                                                                                                                   \n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">                                                                                                                   \n <span style=\"font-weight: bold\"> Progress                 </span> <span style=\"font-weight: bold\"> Draws </span> <span style=\"font-weight: bold\"> Divergences </span> <span style=\"font-weight: bold\"> Step size </span> <span style=\"font-weight: bold\"> Grad evals </span> <span style=\"font-weight: bold\"> Sampling Speed  </span> <span style=\"font-weight: bold\"> Elapsed </span> <span style=\"font-weight: bold\"> Remaining </span> \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             1.257       3            1323.04 draws/s   0:00:01   0:00:00    \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             0.840       3            745.48 draws/s    0:00:02   0:00:00    \n                                                                                                                   \n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "e6c97e7d9cea4703b5573c3f4dc8d1d5": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "5da052e8f9164784a75050f10747c08a": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_7e636c641bd14c5fbb8655481177ee9d",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "                                                                                                                   \n \u001b[1m \u001b[0m\u001b[1mProgress                \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDraws\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDivergences\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mStep size\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mGrad evals\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mSampling Speed \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mRemaining\u001b[0m\u001b[1m \u001b[0m \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             0.411       7            1161.43 draws/s   0:00:01   0:00:00    \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             0.522       7            629.80 draws/s    0:00:03   0:00:00    \n                                                                                                                   \n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">                                                                                                                   \n <span style=\"font-weight: bold\"> Progress                 </span> <span style=\"font-weight: bold\"> Draws </span> <span style=\"font-weight: bold\"> Divergences </span> <span style=\"font-weight: bold\"> Step size </span> <span style=\"font-weight: bold\"> Grad evals </span> <span style=\"font-weight: bold\"> Sampling Speed  </span> <span style=\"font-weight: bold\"> Elapsed </span> <span style=\"font-weight: bold\"> Remaining </span> \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             0.411       7            1161.43 draws/s   0:00:01   0:00:00    \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             0.522       7            629.80 draws/s    0:00:03   0:00:00    \n                                                                                                                   \n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "7e636c641bd14c5fbb8655481177ee9d": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "85eca283c2a146cc83e66e9081292c61": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_f204532cf22f4b56b93d959898bef98d",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Fitting: \u001b[38;2;23;100;244m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━\u001b[0m \u001b[35m 95%\u001b[0m 0:00:01 Average Loss = 142.6\n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Fitting: <span style=\"color: #1764f4; text-decoration-color: #1764f4\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━</span> <span style=\"color: #800080; text-decoration-color: #800080\"> 95%</span> 0:00:01 Average Loss = 142.6\n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "f204532cf22f4b56b93d959898bef98d": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "85469b8bdf4d4d86a68a629504bc069b": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_f3749d99163540ad9d7dcfc99c180f65",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Fitting: \u001b[38;2;23;100;244m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m \u001b[35m 98%\u001b[0m 0:00:01 Average Loss = 0.037478\n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Fitting: <span style=\"color: #1764f4; text-decoration-color: #1764f4\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺</span> <span style=\"color: #800080; text-decoration-color: #800080\"> 98%</span> 0:00:01 Average Loss = 0.037478\n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "f3749d99163540ad9d7dcfc99c180f65": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "fa2f555f5e5447ada62defd5f0a6de35": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_d13b067159974f47b4cc7129cb95af81",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Fitting: \u001b[38;2;23;100;244m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;23;100;244m╸\u001b[0m \u001b[35m100%\u001b[0m 0:00:01 Average Loss = 0.029651\n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Fitting: <span style=\"color: #1764f4; text-decoration-color: #1764f4\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> 0:00:01 Average Loss = 0.029651\n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "d13b067159974f47b4cc7129cb95af81": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "4c8a3c91387a408399cd315eb2a18835": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_1245a827320d4ad78332987695a1c179",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Fitting: \u001b[38;2;23;100;244m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;23;100;244m╸\u001b[0m \u001b[35m 99%\u001b[0m 0:00:01 Average Loss = 0.01763\n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Fitting: <span style=\"color: #1764f4; text-decoration-color: #1764f4\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸</span> <span style=\"color: #800080; text-decoration-color: #800080\"> 99%</span> 0:00:01 Average Loss = 0.01763\n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "1245a827320d4ad78332987695a1c179": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "b6697919f8304d57aeac480d6d852871": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_7da4224189fe421bb4038c0959f502a9",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Fitting \u001b[38;2;23;100;244m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;23;100;244m╸\u001b[0m \u001b[35m100%\u001b[0m 0:00:01\n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Fitting <span style=\"color: #1764f4; text-decoration-color: #1764f4\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> 0:00:01\n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "7da4224189fe421bb4038c0959f502a9": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "0c957dfdcd8c43e9990a0bf9ea3eb45f": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_40ef9fcda6b6494eb9bcc269598b6c68",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "                                                                                                                   \n \u001b[1m \u001b[0m\u001b[1mProgress                \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDraws\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDivergences\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mStep size\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mGrad evals\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mSampling Speed \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mRemaining\u001b[0m\u001b[1m \u001b[0m \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             1.054       3            2101.71 draws/s   0:00:00   0:00:00    \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             1.019       3            916.90 draws/s    0:00:02   0:00:00    \n                                                                                                                   \n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">                                                                                                                   \n <span style=\"font-weight: bold\"> Progress                 </span> <span style=\"font-weight: bold\"> Draws </span> <span style=\"font-weight: bold\"> Divergences </span> <span style=\"font-weight: bold\"> Step size </span> <span style=\"font-weight: bold\"> Grad evals </span> <span style=\"font-weight: bold\"> Sampling Speed  </span> <span style=\"font-weight: bold\"> Elapsed </span> <span style=\"font-weight: bold\"> Remaining </span> \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             1.054       3            2101.71 draws/s   0:00:00   0:00:00    \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             1.019       3            916.90 draws/s    0:00:02   0:00:00    \n                                                                                                                   \n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "40ef9fcda6b6494eb9bcc269598b6c68": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "c0baa173f2b247a9a3605a9a5a155d8a": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_6e4f4d7d80114d2a9c3e72a7d0651a78",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Sampling ... \u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m 0:00:00 / 0:00:00\n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Sampling ... <span style=\"color: #008000; text-decoration-color: #008000\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> 0:00:00 / 0:00:00\n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "6e4f4d7d80114d2a9c3e72a7d0651a78": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "f6147c1b4e6148ad9d9baf0cd6cd1f19": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_248e5af018d24a7abb142d957d7e04ba",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "                                                                                                                   \n \u001b[1m \u001b[0m\u001b[1mProgress                \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDraws\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDivergences\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mStep size\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mGrad evals\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mSampling Speed \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mRemaining\u001b[0m\u001b[1m \u001b[0m \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             1.325       1            2097.52 draws/s   0:00:00   0:00:00    \n  \u001b[38;2;31;119;180m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m   2000    0             1.499       3            1055.87 draws/s   0:00:01   0:00:00    \n                                                                                                                   \n",
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">                                                                                                                   \n <span style=\"font-weight: bold\"> Progress                 </span> <span style=\"font-weight: bold\"> Draws </span> <span style=\"font-weight: bold\"> Divergences </span> <span style=\"font-weight: bold\"> Step size </span> <span style=\"font-weight: bold\"> Grad evals </span> <span style=\"font-weight: bold\"> Sampling Speed  </span> <span style=\"font-weight: bold\"> Elapsed </span> <span style=\"font-weight: bold\"> Remaining </span> \n ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             1.325       1            2097.52 draws/s   0:00:00   0:00:00    \n  <span style=\"color: #1f77b4; text-decoration-color: #1f77b4\">━━━━━━━━━━━━━━━━━━━━━━━━</span>   2000    0             1.499       3            1055.87 draws/s   0:00:01   0:00:00    \n                                                                                                                   \n</pre>\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "248e5af018d24a7abb142d957d7e04ba": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        }
+      }
     }
-   ],
-   "source": [
-    "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    ":::{include} ../page_footer.md\n",
-    ":::"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "jupytext": {
-   "notebook_metadata_filter": "substitutions"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file