diff --git a/.gitignore b/.gitignore
index 37a2d2512..43118ab79 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,3 +11,7 @@ examples/gallery.rst
 
 pixi.lock
 
+
+# pixi environments
+.pixi
+*.egg-info
diff --git a/examples/samplers/fast_sampling_with_jax_and_numba.ipynb b/examples/samplers/fast_sampling_with_jax_and_numba.ipynb
index 45139e3b3..e08895836 100644
--- a/examples/samplers/fast_sampling_with_jax_and_numba.ipynb
+++ b/examples/samplers/fast_sampling_with_jax_and_numba.ipynb
@@ -12,25 +12,74 @@
     ":tags: hierarchical model, JAX, numba, scaling\n",
     ":category: reference, intermediate\n",
     ":author: Thomas Wiecki\n",
-    ":::"
+    ":::\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "PyMC can compile its models to various execution backends through PyTensor, including:\n",
-    "* C\n",
-    "* JAX\n",
-    "* Numba\n",
+    "PyMC offers multiple sampling backends that can dramatically improve performance depending on your model size and requirements. Each backend has distinct advantages and is optimized for different use cases.\n",
     "\n",
-    "By default, PyMC is using the C backend which then gets called by the Python-based samplers.\n",
+    "### PyMC's Built-in Sampler\n",
     "\n",
-    "However, by compiling to other backends, we can use samplers written in other languages than Python that call the PyMC model without any Python-overhead.\n",
+    "```python\n",
+    "pm.sample()\n",
+    "```\n",
     "\n",
-    "For the JAX backend there is the NumPyro and BlackJAX NUTS sampler available. To use these samplers, you have to install `numpyro` and `blackjax`. Both of them are available through conda/mamba: `mamba install -c conda-forge numpyro blackjax`.\n",
+    "The default PyMC sampler uses a Python-based NUTS implementation that provides maximum compatibility with all PyMC features. This sampler is required when working with models that contain discrete variables, as it's the only option that works together with other non-gradient based samplers like Slice and Metropolis. While this sampler can compile the underlying model to different backends (C, Numba, PyTensor or JAX) using PyTensor's compilation system via the `compile_kwargs` parameter, it maintains Python overhead that can limit performance, particularly for small models.\n",
     "\n",
-    "For the Numba backend, there is the [Nutpie sampler](https://github.com/pymc-devs/nutpie) written in Rust. To use this sampler you need `nutpie` installed: `mamba install -c conda-forge nutpie`. "
+    "### Nutpie Sampler\n",
+    "\n",
+    "```python\n",
+    "pm.sample(nuts_sampler=\"nutpie\", nuts_sampler_kwargs={\"backend\": \"numba\"})\n",
+    "pm.sample(nuts_sampler=\"nutpie\", nuts_sampler_kwargs={\"backend\": \"jax\"})\n",
+    "pm.sample(nuts_sampler=\"nutpie\", nuts_sampler_kwargs={\"backend\": \"jax\", \"gradient_backend\": \"pytensor\"})\n",
+    "```\n",
+    "\n",
+    "Nutpie is PyMC's cutting-edge performance sampler. Written in Rust, it eliminates Python overhead and provides exceptional performance for continuous models. In addition, it has an improved NUTS adaptation algorithm that generalizes mass matrix adaptation from affine functions to arbitrary diffeomorphisms. This helps to identify transformations that adapt to the posterior’s scale and shape. The Numba backend typically offers the highest performance for most use cases, while the JAX backend excels with very large models and provides GPU acceleration capabilities. Nutpie is particularly well-suited for production workflows where sampling speed is critical.\n",
+    "\n",
+    "### NumPyro Sampler\n",
+    "\n",
+    "```python\n",
+    "pm.sample(nuts_sampler=\"numpyro\", nuts_sampler_kwargs={\"chain_method\": \"parallel\"})\n",
+    "# GPU-accelerated\n",
+    "pm.sample(nuts_sampler=\"numpyro\", nuts_sampler_kwargs={\"chain_method\": \"vectorized\"})\n",
+    "```\n",
+    "\n",
+    "NumPyro provides a mature JAX-based sampling implementation that integrates seamlessly with the broader JAX ecosystem. This sampler benefits from years of development within the JAX community and provides reliable performance characteristics, with excellent GPU support for accelerated computation.\n",
+    "\n",
+    "### BlackJAX Sampler\n",
+    "\n",
+    "```python\n",
+    "pm.sample(nuts_sampler=\"blackjax\")\n",
+    "```\n",
+    "\n",
+    "BlackJAX offers another JAX-based sampling implementation focused on flexibility and research applications. While it provides similar capabilities to NumPyro, it's less commonly used in production environments. BlackJAX can be valuable for experimental workflows or when specific JAX-based features are required.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation Requirements\n",
+    "\n",
+    "To use the various sampling backends, you need to install the corresponding packages. Nutpie is the recommended high-performance option and can be installed with pip or conda/mamba (e.g. `conda install nutpie`). For JAX-based workflows, NumPyro provides mature functionality and is installed with the `numpyro` package. BlackJAX offers an alternative JAX implementation and is available in the `blackjax` package.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Performance Guidelines\n",
+    "\n",
+    "Understanding when to use each sampler depends on several key factors including model size, variable types, and computational requirements.\n",
+    "\n",
+    "For **small models**, NumPyro typically provides the best balance of performance and reliability. The compilation overhead is minimal, and its mature JAX implementation handles these models efficiently. **Large models** generally perform best with Nutpie's Numba backend for consistent CPU performance or Nutpie's JAX backend when GPU acceleration is needed or memory efficiency is critical.\n",
+    "\n",
+    "Models containing **discrete variables** must use PyMC's built-in sampler, as it's the only implementation that supports compatible (_i.e._, non-gradient based) sampling algorithms. For purely continuous models, all sampling backends are available, making performance the primary consideration.\n",
+    "\n",
+    "**Numba** excels at CPU optimization and provides consistent performance across different model types. It's particularly effective for models with complex mathematical operations that benefit from just-in-time compilation. **JAX** offers superior performance for very large models and provides natural GPU acceleration, making it ideal when computational resources are a limiting factor. The **C** backend serves as a reliable fallback option with broad compatibility but typically offers lower performance than the alternatives.\n"
    ]
   },
   {
@@ -42,17 +91,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on PyMC v5.6.0\n"
+      "Running on PyMC v5.22.0\n"
      ]
     }
    ],
    "source": [
+    "import os\n",
+    "import time\n",
+    "\n",
+    "from collections import defaultdict\n",
+    "\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
+    "import polars as pl\n",
     "import pymc as pm\n",
     "\n",
-    "rng = np.random.default_rng(seed=42)\n",
+    "os.environ[\"XLA_FLAGS\"] = \"--xla_force_host_platform_device_count=4\"\n",
+    "\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "az.style.use(\"arviz-darkgrid\")\n",
+    "\n",
+    "# rng = np.random.default_rng(seed=42)\n",
     "print(f\"Running on PyMC v{pm.__version__}\")"
    ]
   },
@@ -62,15 +122,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%config InlineBackend.figure_format = 'retina'\n",
-    "az.style.use(\"arviz-darkgrid\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will use a simple probabilistic PCA model as our example."
+    "# Dictionary to store all results\n",
+    "results = defaultdict(dict)\n",
+    "\n",
+    "\n",
+    "class TimingContext:\n",
+    "    def __init__(self, name):\n",
+    "        self.name = name\n",
+    "\n",
+    "    def __enter__(self):\n",
+    "        self.start_wall = time.perf_counter()\n",
+    "        self.start_cpu = time.process_time()\n",
+    "        return self\n",
+    "\n",
+    "    def __exit__(self, *args):\n",
+    "        self.end_wall = time.perf_counter()\n",
+    "        self.end_cpu = time.process_time()\n",
+    "\n",
+    "        wall_time = self.end_wall - self.start_wall\n",
+    "        cpu_time = self.end_cpu - self.start_cpu\n",
+    "\n",
+    "        results[self.name][\"wall_time\"] = wall_time\n",
+    "        results[self.name][\"cpu_time\"] = cpu_time\n",
+    "\n",
+    "        print(f\"Wall time: {wall_time:.1f} s\")\n",
+    "        print(f\"CPU time: {cpu_time:.1f} s\")"
    ]
   },
   {
@@ -82,160 +158,195 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "True principal axes:\n",
-      "[[ 0.60943416]\n",
-      " [-2.07996821]]\n"
+      "Generated GP data with 100 points\n",
+      "True hyperparameters: lengthscale=1.0, scale=4.0\n",
+      "Noise: σ=1.0, ν=5.0 (Student-T)\n"
      ]
     }
    ],
    "source": [
-    "def build_toy_dataset(N, D, K, sigma=1):\n",
-    "    x_train = np.zeros((D, N))\n",
-    "    w = rng.normal(\n",
-    "        0.0,\n",
-    "        2.0,\n",
-    "        size=(D, K),\n",
-    "    )\n",
-    "    z = rng.normal(0.0, 1.0, size=(K, N))\n",
-    "    mean = np.dot(w, z)\n",
-    "    for d in range(D):\n",
-    "        for n in range(N):\n",
-    "            x_train[d, n] = rng.normal(mean[d, n], sigma)\n",
+    "def build_gp_latent_dataset(n=200, random_seed=42):\n",
+    "    \"\"\"\n",
+    "    Generate data from a Gaussian Process with Student-T distributed noise.\n",
+    "\n",
+    "    This creates a challenging latent variable problem that tests the samplers'\n",
+    "    ability to efficiently explore the high-dimensional posterior over the\n",
+    "    latent GP function values.\n",
+    "    \"\"\"\n",
+    "    rng_local = np.random.default_rng(random_seed)\n",
+    "\n",
+    "    # Input locations\n",
+    "    X = np.linspace(0, 10, n)[:, None]\n",
+    "\n",
+    "    # True GP hyperparameters\n",
+    "    ell_true = 1.0  # lengthscale\n",
+    "    eta_true = 4.0  # scale\n",
+    "\n",
+    "    # Create true covariance function and sample from GP prior\n",
+    "    cov_func = eta_true**2 * pm.gp.cov.ExpQuad(1, ell_true)\n",
+    "    mean_func = pm.gp.mean.Zero()\n",
+    "\n",
+    "    # Sample latent function values from GP prior with jitter for numerical stability\n",
+    "    K = cov_func(X).eval()\n",
+    "    # Add jitter to diagonal for numerical stability\n",
+    "    K += 1e-6 * np.eye(n)\n",
     "\n",
-    "    print(\"True principal axes:\")\n",
-    "    print(w)\n",
-    "    return x_train\n",
+    "    f_true = pm.draw(pm.MvNormal.dist(mu=mean_func(X), cov=K), 1, random_seed=rng_local)\n",
     "\n",
+    "    # Add Student-T distributed noise (heavier tails than Gaussian)\n",
+    "    sigma_true = 1.0\n",
+    "    nu_true = 5.0  # degrees of freedom\n",
+    "    y = f_true + sigma_true * rng_local.standard_t(df=nu_true, size=n)\n",
     "\n",
-    "N = 5000  # number of data points\n",
-    "D = 2  # data dimensionality\n",
-    "K = 1  # latent dimensionality\n",
+    "    print(f\"Generated GP data with {n} points\")\n",
+    "    print(f\"True hyperparameters: lengthscale={ell_true}, scale={eta_true}\")\n",
+    "    print(f\"Noise: σ={sigma_true}, ν={nu_true} (Student-T)\")\n",
     "\n",
-    "data = build_toy_dataset(N, D, K)"
+    "    return X, y, f_true\n",
+    "\n",
+    "\n",
+    "# Generate the challenging GP dataset\n",
+    "N = 100  # number of data points\n",
+    "X, y_obs, f_true = build_gp_latent_dataset(N)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Simulated data set')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 720x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 491,
-       "width": 731
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "plt.scatter(data[0, :], data[1, :], color=\"blue\", alpha=0.1)\n",
-    "plt.axis([-10, 10, -10, 10])\n",
-    "plt.title(\"Simulated data set\")"
+    "## The Challenge: Latent Gaussian Process Regression\n",
+    "\n",
+    "To properly evaluate the performance differences between sampling backends, we need a model that presents genuine computational challenges. Our test case is a **latent Gaussian Process (GP) regression** with Student-T distributed noise—a model that creates several difficulties for MCMC samplers:\n",
+    "\n",
+    "### Why This Model Is Challenging\n",
+    "\n",
+    "1. **High-dimensional latent space**: The model includes 200 latent function values as parameters, creating a high-dimensional posterior to explore.\n",
+    "\n",
+    "2. **Complex posterior correlations**: The GP prior induces strong correlations between nearby function values through the covariance matrix, making the posterior geometry complex.\n",
+    "\n",
+    "3. **Non-Gaussian likelihood**: The Student-T likelihood has heavier tails than Gaussian noise, requiring robust sampling of outlier-sensitive parameters.\n",
+    "\n",
+    "4. **Hierarchical structure**: The model includes hyperparameters (lengthscale, scale, noise parameters) that control the GP behavior, creating additional dependencies.\n",
+    "\n",
+    "5. **Computational intensity**: Each likelihood evaluation requires computing with a 200×200 covariance matrix, making efficient linear algebra crucial.\n",
+    "\n",
+    "This combination creates a realistic test case where different sampling backends' strengths and weaknesses become apparent. The model is representative of many real-world applications in machine learning, spatial statistics, and time series analysis.\n",
+    "\n",
+    "### Model Structure\n",
+    "\n",
+    "Our latent GP model places a Gaussian Process prior on an unknown function f(x), then observes noisy measurements:\n",
+    "\n",
+    "- **GP prior**: f(x) ~ GP(0, k(x,x')) with squared exponential covariance\n",
+    "- **Hyperpriors**: Lengthscale ~ Gamma(2,1), Scale ~ HalfNormal(5)  \n",
+    "- **Noise model**: y ~ StudentT(f(x), σ, ν) with σ ~ HalfNormal(2), ν ~ 1+Gamma(2,0.1)\n",
+    "\n",
+    "The latent function values f are sampled directly (not marginalized), creating the computational challenge that distinguishes different sampling backends."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "with pm.Model() as PPCA:\n",
-    "    w = pm.Normal(\"w\", mu=0, sigma=2, shape=[D, K], transform=pm.distributions.transforms.Ordered())\n",
-    "    z = pm.Normal(\"z\", mu=0, sigma=1, shape=[N, K])\n",
-    "    x = pm.Normal(\"x\", mu=w.dot(z.T), sigma=1, shape=[D, N], observed=data)"
+    "def gp_latent_model():\n",
+    "\n",
+    "    with pm.Model() as model:\n",
+    "        ell = pm.Gamma(\"ell\", alpha=2, beta=1)\n",
+    "        eta = pm.HalfNormal(\"eta\", sigma=5)\n",
+    "\n",
+    "        cov = eta**2 * pm.gp.cov.ExpQuad(1, ell)\n",
+    "        gp = pm.gp.Latent(cov_func=cov)\n",
+    "\n",
+    "        f = gp.prior(\"f\", X=X)\n",
+    "\n",
+    "        sigma = pm.HalfNormal(\"sigma\", sigma=2.0)\n",
+    "        nu = 1 + pm.Gamma(\"nu\", alpha=2, beta=0.1)\n",
+    "\n",
+    "        _ = pm.StudentT(\"y\", mu=f, lam=1.0 / sigma, nu=nu, observed=y_obs)\n",
+    "    return model"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Sampling using Python NUTS sampler"
+    "## Performance Comparison\n",
+    "\n",
+    "Now let's compare the performance of different sampling backends on our challenging latent GP model. We'll measure sampling speed and efficiency, in terms of effective samples drawn.\n",
+    "\n",
+    "### 1. PyTensor Default Sampler"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [w, z]\n"
+      "NUTS: [ell, eta, f_rotated_, sigma, nu]\n",
+      "Sampling 4 chains for 1_000 tune and 250 draw iterations (4_000 + 1_000 draws total) took 66 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:28&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 69.5 s\n",
+      "CPU time: 6.6 s\n",
+      "Min ESS: 138, Mean ESS: 475\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_draws = 250\n",
+    "n_tune = 1000\n",
+    "n_chains = 4\n",
+    "\n",
+    "model = gp_latent_model()\n",
+    "with TimingContext(\"PyTensor Default\"):\n",
+    "    with model:\n",
+    "        idata_pytensor_default = pm.sample(\n",
+    "            draws=n_draws, tune=n_tune, chains=n_chains, progressbar=False\n",
+    "        )\n",
+    "\n",
+    "ess_pytensor_default = az.ess(idata_pytensor_default)\n",
+    "min_ess = min([ess_pytensor_default[var].values.min() for var in ess_pytensor_default.data_vars])\n",
+    "mean_ess = np.mean(\n",
+    "    [ess_pytensor_default[var].values.mean() for var in ess_pytensor_default.data_vars]\n",
+    ")\n",
+    "results[\"PyTensor Default\"][\"min_ess\"] = min_ess\n",
+    "results[\"PyTensor Default\"][\"mean_ess\"] = mean_ess\n",
+    "print(f\"Min ESS: {min_ess:.0f}, Mean ESS: {mean_ess:.0f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. PyTensor Sampler with Numba Backend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 29 seconds.\n",
-      "/Users/twiecki/micromamba/envs/pymc5/lib/python3.11/site-packages/arviz/utils.py:184: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
-      "  numba_fn = numba.jit(**self.kwargs)(self.function)\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [ell, eta, f_rotated_, sigma, nu]\n",
+      "Sampling 4 chains for 1_000 tune and 250 draw iterations (4_000 + 1_000 draws total) took 69 seconds.\n",
       "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
       "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
      ]
@@ -244,22 +355,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 19.7 s, sys: 971 ms, total: 20.7 s\n",
-      "Wall time: 47.6 s\n"
+      "Wall time: 95.2 s\n",
+      "CPU time: 29.8 s\n",
+      "Min ESS: 10, Mean ESS: 308\n"
      ]
     }
    ],
    "source": [
-    "%%time\n",
-    "with PPCA:\n",
-    "    idata_pymc = pm.sample()"
+    "n_draws = 250\n",
+    "n_tune = 1000\n",
+    "n_chains = 4\n",
+    "\n",
+    "model = gp_latent_model()\n",
+    "with TimingContext(\"PyTensor Numba\"):\n",
+    "    with model:\n",
+    "        idata_pytensor_numba = pm.sample(\n",
+    "            draws=n_draws,\n",
+    "            tune=n_tune,\n",
+    "            chains=n_chains,\n",
+    "            compile_kwargs={\"mode\": \"numba\"},\n",
+    "            progressbar=False,\n",
+    "        )\n",
+    "\n",
+    "ess_pytensor_numba = az.ess(idata_pytensor_numba)\n",
+    "min_ess = min([ess_pytensor_numba[var].values.min() for var in ess_pytensor_numba.data_vars])\n",
+    "mean_ess = np.mean([ess_pytensor_numba[var].values.mean() for var in ess_pytensor_numba.data_vars])\n",
+    "results[\"PyTensor Numba\"][\"min_ess\"] = min_ess\n",
+    "results[\"PyTensor Numba\"][\"mean_ess\"] = mean_ess\n",
+    "print(f\"Min ESS: {min_ess:.0f}, Mean ESS: {mean_ess:.0f}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Sampling using NumPyro JAX NUTS sampler"
+    "### 3. PyTensor with PyTorch Backend"
    ]
   },
   {
@@ -271,153 +401,549 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/twiecki/micromamba/envs/pymc5/lib/python3.11/site-packages/pymc/sampling/mcmc.py:273: UserWarning: Use of external NUTS sampler is still experimental\n",
-      "  warnings.warn(\"Use of external NUTS sampler is still experimental\", UserWarning)\n",
-      "/Users/twiecki/micromamba/envs/pymc5/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/link/pytorch/dispatch/basic.py:38: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /home/conda/feedstock_root/build_artifacts/libtorch_1746251340001/work/torch/csrc/utils/tensor_numpy.cpp:203.)\n",
+      "  return torch.as_tensor(data, dtype=dtype)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Compiling...\n",
-      "Compilation time =  0:00:00.619901\n",
-      "Sampling...\n",
-      "Sampling time =  0:00:11.469112\n",
-      "Transforming variables...\n",
-      "Transformation time =  0:00:00.118111\n",
-      "CPU times: user 40.5 s, sys: 6.66 s, total: 47.2 s\n",
-      "Wall time: 12.9 s\n"
+      "Wall time: 2.7 s\n",
+      "CPU time: 3.7 s\n"
+     ]
+    },
+    {
+     "ename": "NotImplementedError",
+     "evalue": "Dispatch not implemented for Scalar Op clip",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNotImplementedError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m TimingContext(\u001b[33m\"\u001b[39m\u001b[33mPyTensor PyTorch\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m      7\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m model:\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m         idata_pytensor_pytorch = \u001b[43mpm\u001b[49m\u001b[43m.\u001b[49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdraws\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn_draws\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtune\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn_tune\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchains\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn_chains\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompile_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmode\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mpytorch\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogressbar\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m     10\u001b[39m ess_pytensor_pytorch = az.ess(idata_pytensor_pytorch)\n\u001b[32m     11\u001b[39m min_ess = \u001b[38;5;28mmin\u001b[39m([ess_pytensor_pytorch[var].values.min() \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m ess_pytensor_pytorch.data_vars])\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/sampling/mcmc.py:832\u001b[39m, in \u001b[36msample\u001b[39m\u001b[34m(draws, tune, chains, cores, random_seed, progressbar, progressbar_theme, step, var_names, nuts_sampler, initvals, init, jitter_max_retries, n_init, trace, discard_tuned_samples, compute_convergence_checks, keep_warning_stat, return_inferencedata, idata_kwargs, nuts_sampler_kwargs, callback, mp_ctx, blas_cores, model, compile_kwargs, **kwargs)\u001b[39m\n\u001b[32m    830\u001b[39m         [kwargs.setdefault(k, v) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m nuts_kwargs.items()]\n\u001b[32m    831\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m joined_blas_limiter():\n\u001b[32m--> \u001b[39m\u001b[32m832\u001b[39m         initial_points, step = \u001b[43minit_nuts\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    833\u001b[39m \u001b[43m            \u001b[49m\u001b[43minit\u001b[49m\u001b[43m=\u001b[49m\u001b[43minit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    834\u001b[39m \u001b[43m            \u001b[49m\u001b[43mchains\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchains\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    835\u001b[39m \u001b[43m            \u001b[49m\u001b[43mn_init\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    836\u001b[39m \u001b[43m            \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    837\u001b[39m \u001b[43m            \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrandom_seed_list\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    838\u001b[39m \u001b[43m            \u001b[49m\u001b[43mprogressbar\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprogress_bool\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    839\u001b[39m \u001b[43m            \u001b[49m\u001b[43mjitter_max_retries\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjitter_max_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    840\u001b[39m \u001b[43m            \u001b[49m\u001b[43mtune\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtune\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    841\u001b[39m \u001b[43m            \u001b[49m\u001b[43minitvals\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitvals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    842\u001b[39m \u001b[43m            \u001b[49m\u001b[43mcompile_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcompile_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    843\u001b[39m \u001b[43m            \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    844\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    845\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    846\u001b[39m     \u001b[38;5;66;03m# Get initial points\u001b[39;00m\n\u001b[32m    847\u001b[39m     ipfns = make_initial_point_fns_per_chain(\n\u001b[32m    848\u001b[39m         model=model,\n\u001b[32m    849\u001b[39m         overrides=initvals,\n\u001b[32m    850\u001b[39m         jitter_rvs=\u001b[38;5;28mset\u001b[39m(),\n\u001b[32m    851\u001b[39m         chains=chains,\n\u001b[32m    852\u001b[39m     )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/sampling/mcmc.py:1598\u001b[39m, in \u001b[36minit_nuts\u001b[39m\u001b[34m(init, chains, n_init, model, random_seed, progressbar, jitter_max_retries, tune, initvals, compile_kwargs, **kwargs)\u001b[39m\n\u001b[32m   1592\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33madvi\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m init:\n\u001b[32m   1593\u001b[39m     cb = [\n\u001b[32m   1594\u001b[39m         pm.callbacks.CheckParametersConvergence(tolerance=\u001b[32m1e-2\u001b[39m, diff=\u001b[33m\"\u001b[39m\u001b[33mabsolute\u001b[39m\u001b[33m\"\u001b[39m),\n\u001b[32m   1595\u001b[39m         pm.callbacks.CheckParametersConvergence(tolerance=\u001b[32m1e-2\u001b[39m, diff=\u001b[33m\"\u001b[39m\u001b[33mrelative\u001b[39m\u001b[33m\"\u001b[39m),\n\u001b[32m   1596\u001b[39m     ]\n\u001b[32m-> \u001b[39m\u001b[32m1598\u001b[39m logp_dlogp_func = \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlogp_dlogp_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mravel_inputs\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mcompile_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1599\u001b[39m logp_dlogp_func.trust_input = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m   1601\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[34mmodel_logp_fn\u001b[39m(ip: PointType) -> np.ndarray:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/model/core.py:572\u001b[39m, in \u001b[36mModel.logp_dlogp_function\u001b[39m\u001b[34m(self, grad_vars, tempered, initial_point, ravel_inputs, **kwargs)\u001b[39m\n\u001b[32m    566\u001b[39m     initial_point = \u001b[38;5;28mself\u001b[39m.initial_point(\u001b[32m0\u001b[39m)\n\u001b[32m    567\u001b[39m extra_vars_and_values = {\n\u001b[32m    568\u001b[39m     var: initial_point[var.name]\n\u001b[32m    569\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.value_vars\n\u001b[32m    570\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m input_vars \u001b[38;5;129;01mand\u001b[39;00m var \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m grad_vars\n\u001b[32m    571\u001b[39m }\n\u001b[32m--> \u001b[39m\u001b[32m572\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mValueGradFunction\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    573\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcosts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    574\u001b[39m \u001b[43m    \u001b[49m\u001b[43mgrad_vars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    575\u001b[39m \u001b[43m    \u001b[49m\u001b[43mextra_vars_and_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    576\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    577\u001b[39m \u001b[43m    \u001b[49m\u001b[43minitial_point\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_point\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    578\u001b[39m \u001b[43m    \u001b[49m\u001b[43mravel_inputs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mravel_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    579\u001b[39m \u001b[43m    \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    580\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/model/core.py:256\u001b[39m, in \u001b[36mValueGradFunction.__init__\u001b[39m\u001b[34m(self, costs, grad_vars, extra_vars_and_values, dtype, casting, compute_grads, model, initial_point, ravel_inputs, **kwargs)\u001b[39m\n\u001b[32m    250\u001b[39m         warnings.warn(\n\u001b[32m    251\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mValueGradFunction will become a function of raveled inputs.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    252\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mSpecify `ravel_inputs` to suppress this warning. Note that setting `ravel_inputs=False` will be forbidden in a future release.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    253\u001b[39m         )\n\u001b[32m    254\u001b[39m     inputs = grad_vars\n\u001b[32m--> \u001b[39m\u001b[32m256\u001b[39m \u001b[38;5;28mself\u001b[39m._pytensor_function = \u001b[38;5;28;43mcompile\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgivens\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgivens\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    257\u001b[39m \u001b[38;5;28mself\u001b[39m._raveled_inputs = ravel_inputs\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/pytensorf.py:947\u001b[39m, in \u001b[36mcompile\u001b[39m\u001b[34m(inputs, outputs, random_seed, mode, **kwargs)\u001b[39m\n\u001b[32m    945\u001b[39m opt_qry = mode.provided_optimizer.including(\u001b[33m\"\u001b[39m\u001b[33mrandom_make_inplace\u001b[39m\u001b[33m\"\u001b[39m, check_parameter_opt)\n\u001b[32m    946\u001b[39m mode = Mode(linker=mode.linker, optimizer=opt_qry)\n\u001b[32m--> \u001b[39m\u001b[32m947\u001b[39m pytensor_function = \u001b[43mpytensor\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    948\u001b[39m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    949\u001b[39m \u001b[43m    \u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    950\u001b[39m \u001b[43m    \u001b[49m\u001b[43mupdates\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mrng_updates\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mupdates\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    951\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    952\u001b[39m \u001b[43m    \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    953\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    954\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m pytensor_function\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/compile/function/__init__.py:332\u001b[39m, in \u001b[36mfunction\u001b[39m\u001b[34m(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, trust_input)\u001b[39m\n\u001b[32m    321\u001b[39m     fn = orig_function(\n\u001b[32m    322\u001b[39m         inputs,\n\u001b[32m    323\u001b[39m         outputs,\n\u001b[32m   (...)\u001b[39m\u001b[32m    327\u001b[39m         trust_input=trust_input,\n\u001b[32m    328\u001b[39m     )\n\u001b[32m    329\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    330\u001b[39m     \u001b[38;5;66;03m# note: pfunc will also call orig_function -- orig_function is\u001b[39;00m\n\u001b[32m    331\u001b[39m     \u001b[38;5;66;03m#      a choke point that all compilation must pass through\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m332\u001b[39m     fn = \u001b[43mpfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    333\u001b[39m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m=\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    334\u001b[39m \u001b[43m        \u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    335\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    336\u001b[39m \u001b[43m        \u001b[49m\u001b[43mupdates\u001b[49m\u001b[43m=\u001b[49m\u001b[43mupdates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    337\u001b[39m \u001b[43m        \u001b[49m\u001b[43mgivens\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgivens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    338\u001b[39m \u001b[43m        \u001b[49m\u001b[43mno_default_updates\u001b[49m\u001b[43m=\u001b[49m\u001b[43mno_default_updates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    339\u001b[39m \u001b[43m        \u001b[49m\u001b[43maccept_inplace\u001b[49m\u001b[43m=\u001b[49m\u001b[43maccept_inplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    340\u001b[39m \u001b[43m        \u001b[49m\u001b[43mname\u001b[49m\u001b[43m=\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    341\u001b[39m \u001b[43m        \u001b[49m\u001b[43mrebuild_strict\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrebuild_strict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    342\u001b[39m \u001b[43m        \u001b[49m\u001b[43mallow_input_downcast\u001b[49m\u001b[43m=\u001b[49m\u001b[43mallow_input_downcast\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    343\u001b[39m \u001b[43m        \u001b[49m\u001b[43mon_unused_input\u001b[49m\u001b[43m=\u001b[49m\u001b[43mon_unused_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    344\u001b[39m \u001b[43m        \u001b[49m\u001b[43mprofile\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprofile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    345\u001b[39m \u001b[43m        \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    346\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtrust_input\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrust_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    347\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    348\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m fn\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/compile/function/pfunc.py:466\u001b[39m, in \u001b[36mpfunc\u001b[39m\u001b[34m(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys, fgraph, trust_input)\u001b[39m\n\u001b[32m    452\u001b[39m     profile = ProfileStats(message=profile)\n\u001b[32m    454\u001b[39m inputs, cloned_outputs = construct_pfunc_ins_and_outs(\n\u001b[32m    455\u001b[39m     params,\n\u001b[32m    456\u001b[39m     outputs,\n\u001b[32m   (...)\u001b[39m\u001b[32m    463\u001b[39m     fgraph=fgraph,\n\u001b[32m    464\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m466\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43morig_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    467\u001b[39m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    468\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcloned_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    469\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    470\u001b[39m \u001b[43m    \u001b[49m\u001b[43maccept_inplace\u001b[49m\u001b[43m=\u001b[49m\u001b[43maccept_inplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    471\u001b[39m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[43m=\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    472\u001b[39m \u001b[43m    \u001b[49m\u001b[43mprofile\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprofile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    473\u001b[39m \u001b[43m    \u001b[49m\u001b[43mon_unused_input\u001b[49m\u001b[43m=\u001b[49m\u001b[43mon_unused_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    474\u001b[39m \u001b[43m    \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    475\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfgraph\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    476\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtrust_input\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrust_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    477\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/compile/function/types.py:1833\u001b[39m, in \u001b[36morig_function\u001b[39m\u001b[34m(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys, fgraph, trust_input)\u001b[39m\n\u001b[32m   1820\u001b[39m     m = Maker(\n\u001b[32m   1821\u001b[39m         inputs,\n\u001b[32m   1822\u001b[39m         outputs,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1830\u001b[39m         trust_input=trust_input,\n\u001b[32m   1831\u001b[39m     )\n\u001b[32m   1832\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m config.change_flags(compute_test_value=\u001b[33m\"\u001b[39m\u001b[33moff\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m-> \u001b[39m\u001b[32m1833\u001b[39m         fn = \u001b[43mm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdefaults\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1834\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m   1835\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m profile \u001b[38;5;129;01mand\u001b[39;00m fn:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/compile/function/types.py:1717\u001b[39m, in \u001b[36mFunctionMaker.create\u001b[39m\u001b[34m(self, input_storage, storage_map)\u001b[39m\n\u001b[32m   1714\u001b[39m start_import_time = pytensor.link.c.cmodule.import_time\n\u001b[32m   1716\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m config.change_flags(traceback__limit=config.traceback__compile_limit):\n\u001b[32m-> \u001b[39m\u001b[32m1717\u001b[39m     _fn, _i, _o = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlinker\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmake_thunk\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1718\u001b[39m \u001b[43m        \u001b[49m\u001b[43minput_storage\u001b[49m\u001b[43m=\u001b[49m\u001b[43minput_storage_lists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_map\u001b[49m\n\u001b[32m   1719\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1721\u001b[39m end_linker = time.perf_counter()\n\u001b[32m   1723\u001b[39m linker_time = end_linker - start_linker\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/link/basic.py:245\u001b[39m, in \u001b[36mLocalLinker.make_thunk\u001b[39m\u001b[34m(self, input_storage, output_storage, storage_map, **kwargs)\u001b[39m\n\u001b[32m    238\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[34mmake_thunk\u001b[39m(\n\u001b[32m    239\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m    240\u001b[39m     input_storage: Optional[\u001b[33m\"\u001b[39m\u001b[33mInputStorageType\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m   (...)\u001b[39m\u001b[32m    243\u001b[39m     **kwargs,\n\u001b[32m    244\u001b[39m ) -> \u001b[38;5;28mtuple\u001b[39m[\u001b[33m\"\u001b[39m\u001b[33mBasicThunkType\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mInputStorageType\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mOutputStorageType\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m--> \u001b[39m\u001b[32m245\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmake_all\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    246\u001b[39m \u001b[43m        \u001b[49m\u001b[43minput_storage\u001b[49m\u001b[43m=\u001b[49m\u001b[43minput_storage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    247\u001b[39m \u001b[43m        \u001b[49m\u001b[43moutput_storage\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_storage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    248\u001b[39m \u001b[43m        \u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    249\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m[:\u001b[32m3\u001b[39m]\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/link/basic.py:695\u001b[39m, in \u001b[36mJITLinker.make_all\u001b[39m\u001b[34m(self, input_storage, output_storage, storage_map)\u001b[39m\n\u001b[32m    692\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m storage_map:\n\u001b[32m    693\u001b[39m     compute_map[k] = [k.owner \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m]\n\u001b[32m--> \u001b[39m\u001b[32m695\u001b[39m thunks, nodes, jit_fn = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcreate_jitable_thunk\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    696\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcompute_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnodes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_storage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_storage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage_map\u001b[49m\n\u001b[32m    697\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    699\u001b[39m [fn] = thunks\n\u001b[32m    700\u001b[39m fn.jit_fn = jit_fn\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/link/basic.py:647\u001b[39m, in \u001b[36mJITLinker.create_jitable_thunk\u001b[39m\u001b[34m(self, compute_map, order, input_storage, output_storage, storage_map)\u001b[39m\n\u001b[32m    644\u001b[39m \u001b[38;5;66;03m# This is a bit hackish, but we only return one of the output nodes\u001b[39;00m\n\u001b[32m    645\u001b[39m output_nodes = [o.owner \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.fgraph.outputs \u001b[38;5;28;01mif\u001b[39;00m o.owner \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m][:\u001b[32m1\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m647\u001b[39m converted_fgraph = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfgraph_convert\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    648\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    649\u001b[39m \u001b[43m    \u001b[49m\u001b[43morder\u001b[49m\u001b[43m=\u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    650\u001b[39m \u001b[43m    \u001b[49m\u001b[43minput_storage\u001b[49m\u001b[43m=\u001b[49m\u001b[43minput_storage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    651\u001b[39m \u001b[43m    \u001b[49m\u001b[43moutput_storage\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_storage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    652\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    653\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    655\u001b[39m thunk_inputs = \u001b[38;5;28mself\u001b[39m.create_thunk_inputs(storage_map)\n\u001b[32m    656\u001b[39m thunk_outputs = [storage_map[n] \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.fgraph.outputs]\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/link/pytorch/linker.py:33\u001b[39m, in \u001b[36mPytorchLinker.fgraph_convert\u001b[39m\u001b[34m(self, fgraph, input_storage, storage_map, **kwargs)\u001b[39m\n\u001b[32m     26\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m functor\n\u001b[32m     28\u001b[39m built_kwargs = {\n\u001b[32m     29\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33munique_name\u001b[39m\u001b[33m\"\u001b[39m: generator,\n\u001b[32m     30\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mconversion_func\u001b[39m\u001b[33m\"\u001b[39m: conversion_func_register,\n\u001b[32m     31\u001b[39m     **kwargs,\n\u001b[32m     32\u001b[39m }\n\u001b[32m---> \u001b[39m\u001b[32m33\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpytorch_funcify\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     34\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfgraph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_storage\u001b[49m\u001b[43m=\u001b[49m\u001b[43minput_storage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mbuilt_kwargs\u001b[49m\n\u001b[32m     35\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/functools.py:912\u001b[39m, in \u001b[36msingledispatch.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kw)\u001b[39m\n\u001b[32m    908\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[32m    909\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfuncname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m requires at least \u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m    910\u001b[39m                     \u001b[33m'\u001b[39m\u001b[33m1 positional argument\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m912\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__class__\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/link/pytorch/dispatch/basic.py:65\u001b[39m, in \u001b[36mpytorch_funcify_FunctionGraph\u001b[39m\u001b[34m(fgraph, node, fgraph_name, conversion_func, **kwargs)\u001b[39m\n\u001b[32m     56\u001b[39m \u001b[38;5;129m@pytorch_funcify\u001b[39m.register(FunctionGraph)\n\u001b[32m     57\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[34mpytorch_funcify_FunctionGraph\u001b[39m(\n\u001b[32m     58\u001b[39m     fgraph,\n\u001b[32m   (...)\u001b[39m\u001b[32m     62\u001b[39m     **kwargs,\n\u001b[32m     63\u001b[39m ):\n\u001b[32m     64\u001b[39m     built_kwargs = {\u001b[33m\"\u001b[39m\u001b[33mconversion_func\u001b[39m\u001b[33m\"\u001b[39m: conversion_func, **kwargs}\n\u001b[32m---> \u001b[39m\u001b[32m65\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfgraph_to_python\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     66\u001b[39m \u001b[43m        \u001b[49m\u001b[43mfgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     67\u001b[39m \u001b[43m        \u001b[49m\u001b[43mconversion_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     68\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtype_conversion_fn\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpytorch_typify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     69\u001b[39m \u001b[43m        \u001b[49m\u001b[43mfgraph_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfgraph_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     70\u001b[39m \u001b[43m        \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mbuilt_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     71\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/link/utils.py:736\u001b[39m, in \u001b[36mfgraph_to_python\u001b[39m\u001b[34m(fgraph, op_conversion_fn, type_conversion_fn, order, storage_map, fgraph_name, global_env, local_env, get_name_for_object, squeeze_output, unique_name, **kwargs)\u001b[39m\n\u001b[32m    734\u001b[39m body_assigns = []\n\u001b[32m    735\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m order:\n\u001b[32m--> \u001b[39m\u001b[32m736\u001b[39m     compiled_func = \u001b[43mop_conversion_fn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    737\u001b[39m \u001b[43m        \u001b[49m\u001b[43mnode\u001b[49m\u001b[43m.\u001b[49m\u001b[43mop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m    738\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    740\u001b[39m     \u001b[38;5;66;03m# Create a local alias with a unique name\u001b[39;00m\n\u001b[32m    741\u001b[39m     local_compiled_func_name = unique_name(compiled_func)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/link/pytorch/linker.py:23\u001b[39m, in \u001b[36mPytorchLinker.fgraph_convert.<locals>.conversion_func_register\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m     22\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[34mconversion_func_register\u001b[39m(*args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m     functor = \u001b[43mpytorch_funcify\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     24\u001b[39m     name = kwargs[\u001b[33m\"\u001b[39m\u001b[33munique_name\u001b[39m\u001b[33m\"\u001b[39m](functor)\n\u001b[32m     25\u001b[39m     \u001b[38;5;28mself\u001b[39m.gen_functors.append((\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m, functor))\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/functools.py:912\u001b[39m, in \u001b[36msingledispatch.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kw)\u001b[39m\n\u001b[32m    908\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[32m    909\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfuncname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m requires at least \u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m    910\u001b[39m                     \u001b[33m'\u001b[39m\u001b[33m1 positional argument\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m912\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__class__\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/link/pytorch/dispatch/elemwise.py:16\u001b[39m, in \u001b[36mpytorch_funcify_Elemwise\u001b[39m\u001b[34m(op, node, **kwargs)\u001b[39m\n\u001b[32m     12\u001b[39m \u001b[38;5;129m@pytorch_funcify\u001b[39m.register(Elemwise)\n\u001b[32m     13\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[34mpytorch_funcify_Elemwise\u001b[39m(op, node, **kwargs):\n\u001b[32m     14\u001b[39m     scalar_op = op.scalar_op\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m     base_fn = \u001b[43mpytorch_funcify\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscalar_op\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     18\u001b[39m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[34mcheck_special_scipy\u001b[39m(func_name):\n\u001b[32m     19\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mscipy.\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m func_name:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/functools.py:912\u001b[39m, in \u001b[36msingledispatch.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kw)\u001b[39m\n\u001b[32m    908\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[32m    909\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfuncname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m requires at least \u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m    910\u001b[39m                     \u001b[33m'\u001b[39m\u001b[33m1 positional argument\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m912\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__class__\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/link/pytorch/dispatch/scalar.py:30\u001b[39m, in \u001b[36mpytorch_funcify_ScalarOp\u001b[39m\u001b[34m(op, node, **kwargs)\u001b[39m\n\u001b[32m     28\u001b[39m nfunc_spec = \u001b[38;5;28mgetattr\u001b[39m(op, \u001b[33m\"\u001b[39m\u001b[33mnfunc_spec\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m     29\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m nfunc_spec \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m30\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mDispatch not implemented for Scalar Op \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mop\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m     32\u001b[39m func_name = nfunc_spec[\u001b[32m0\u001b[39m].replace(\u001b[33m\"\u001b[39m\u001b[33mscipy.\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     34\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m func_name:\n",
+      "\u001b[31mNotImplementedError\u001b[39m: Dispatch not implemented for Scalar Op clip"
      ]
     }
    ],
    "source": [
-    "%%time\n",
-    "with PPCA:\n",
-    "    idata_numpyro = pm.sample(nuts_sampler=\"numpyro\", progressbar=False)"
+    "n_draws = 250\n",
+    "n_tune = 1000\n",
+    "n_chains = 4\n",
+    "\n",
+    "model = gp_latent_model()\n",
+    "with TimingContext(\"PyTensor PyTorch\"):\n",
+    "    with model:\n",
+    "        idata_pytensor_pytorch = pm.sample(\n",
+    "            draws=n_draws,\n",
+    "            tune=n_tune,\n",
+    "            chains=n_chains,\n",
+    "            compile_kwargs={\"mode\": \"pytorch\"},\n",
+    "            progressbar=False,\n",
+    "        )\n",
+    "\n",
+    "ess_pytensor_pytorch = az.ess(idata_pytensor_pytorch)\n",
+    "min_ess = min([ess_pytensor_pytorch[var].values.min() for var in ess_pytensor_pytorch.data_vars])\n",
+    "mean_ess = np.mean(\n",
+    "    [ess_pytensor_pytorch[var].values.mean() for var in ess_pytensor_pytorch.data_vars]\n",
+    ")\n",
+    "results[\"PyTensor PyTorch\"][\"min_ess\"] = min_ess\n",
+    "results[\"PyTensor PyTorch\"][\"mean_ess\"] = mean_ess\n",
+    "print(f\"Min ESS: {min_ess:.0f}, Mean ESS: {mean_ess:.0f}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Sampling using BlackJAX NUTS sampler"
+    "### 4. Nutpie Sampler with Numba Backend\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 278.0 s\n",
+      "CPU time: 3117.2 s\n",
+      "Min ESS: 147, Mean ESS: 830\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = gp_latent_model()\n",
+    "with TimingContext(\"Nutpie Numba\"):\n",
+    "    with model:\n",
+    "        idata_nutpie_numba = pm.sample(\n",
+    "            draws=n_draws,\n",
+    "            tune=n_tune,\n",
+    "            chains=n_chains,\n",
+    "            nuts_sampler=\"nutpie\",\n",
+    "            nuts_sampler_kwargs={\"backend\": \"numba\"},\n",
+    "            progressbar=False,\n",
+    "        )\n",
+    "\n",
+    "ess_nutpie_numba = az.ess(idata_nutpie_numba)\n",
+    "min_ess = min([ess_nutpie_numba[var].values.min() for var in ess_nutpie_numba.data_vars])\n",
+    "mean_ess = np.mean([ess_nutpie_numba[var].values.mean() for var in ess_nutpie_numba.data_vars])\n",
+    "results[\"Nutpie Numba\"][\"min_ess\"] = min_ess\n",
+    "results[\"Nutpie Numba\"][\"mean_ess\"] = mean_ess\n",
+    "print(f\"Min ESS: {min_ess:.0f}, Mean ESS: {mean_ess:.0f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Nutpie Sampler with JAX Backend\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/twiecki/micromamba/envs/pymc5/lib/python3.11/site-packages/pymc/sampling/mcmc.py:273: UserWarning: Use of external NUTS sampler is still experimental\n",
-      "  warnings.warn(\"Use of external NUTS sampler is still experimental\", UserWarning)\n"
+      "/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/model/fgraph.py:163: UserWarning: Detected variables likely created by GP objects. Further use of these old GP objects should be avoided as it may reintroduce variables from the old model. See issue: https://github.com/pymc-devs/pymc/issues/6883\n",
+      "  warnings.warn(\n",
+      "arviz - WARNING - Array contains NaN-value.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Compiling...\n",
-      "Compilation time =  0:00:00.607693\n",
-      "Sampling...\n",
-      "Sampling time =  0:00:02.132882\n",
-      "Transforming variables...\n",
-      "Transformation time =  0:00:08.410508\n",
-      "CPU times: user 35.4 s, sys: 6.73 s, total: 42.1 s\n",
-      "Wall time: 11.6 s\n"
+      "Wall time: 4282.5 s\n",
+      "CPU time: 63076.6 s\n",
+      "Min ESS: nan, Mean ESS: nan\n"
      ]
     }
    ],
    "source": [
-    "%%time\n",
-    "with PPCA:\n",
-    "    idata_blackjax = pm.sample(nuts_sampler=\"blackjax\")"
+    "model = gp_latent_model()\n",
+    "with TimingContext(\"Nutpie JAX\"):\n",
+    "    with model:\n",
+    "        idata_nutpie_jax = pm.sample(\n",
+    "            draws=n_draws,\n",
+    "            tune=n_tune,\n",
+    "            chains=n_chains,\n",
+    "            nuts_sampler=\"nutpie\",\n",
+    "            nuts_sampler_kwargs={\"backend\": \"jax\"},\n",
+    "            progressbar=False,\n",
+    "        )\n",
+    "\n",
+    "ess_nutpie_jax = az.ess(idata_nutpie_jax)\n",
+    "min_ess = min([ess_nutpie_jax[var].values.min() for var in ess_nutpie_jax.data_vars])\n",
+    "mean_ess = np.mean([ess_nutpie_jax[var].values.mean() for var in ess_nutpie_jax.data_vars])\n",
+    "results[\"Nutpie JAX\"][\"min_ess\"] = min_ess\n",
+    "results[\"Nutpie JAX\"][\"mean_ess\"] = mean_ess\n",
+    "print(f\"Min ESS: {min_ess:.0f}, Mean ESS: {mean_ess:.0f}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Sampling using Nutpie Rust NUTS sampler"
+    "### 6. NumPyro Sampler\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/twiecki/micromamba/envs/pymc5/lib/python3.11/site-packages/pymc/sampling/mcmc.py:273: UserWarning: Use of external NUTS sampler is still experimental\n",
-      "  warnings.warn(\"Use of external NUTS sampler is still experimental\", UserWarning)\n",
-      "/Users/twiecki/micromamba/envs/pymc5/lib/python3.11/site-packages/pymc/util.py:501: FutureWarning: The tag attribute observations is deprecated. Use model.rvs_to_values[rv] instead\n",
-      "  warnings.warn(\n"
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 356.8 s\n",
+      "CPU time: 5419.6 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = gp_latent_model()\n",
+    "with TimingContext(\"Numpyro\"):\n",
+    "    with model:\n",
+    "        idata_numpyro = pm.sample(\n",
+    "            draws=n_draws,\n",
+    "            tune=n_tune,\n",
+    "            chains=n_chains,\n",
+    "            nuts_sampler=\"numpyro\",\n",
+    "            nuts_sampler_kwargs={\"chain_method\": \"parallel\"},\n",
+    "            progressbar=False,\n",
+    "        )\n",
+    "\n",
+    "ess_numpyro = az.ess(idata_numpyro)\n",
+    "min_ess = min([ess_numpyro[var].values.min() for var in ess_numpyro.data_vars])\n",
+    "mean_ess = np.mean([ess_numpyro[var].values.mean() for var in ess_numpyro.data_vars])\n",
+    "results[\"Numpyro\"][\"min_ess\"] = min_ess\n",
+    "results[\"Numpyro\"][\"mean_ess\"] = mean_ess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Raw ESS/sec values (for debugging):\n",
+      "PyTensor Default: 6.84\n",
+      "PyTensor Numba: 3.23\n",
+      "Numpyro: 3.00\n",
+      "Nutpie Numba: 2.99\n",
+      "\n",
+      "Performance Summary Table:\n",
+      "====================================================================================================\n",
+      "Sampling Backend  Wall Time (s) CPU Time (s) Min ESS Mean ESS ESS/sec Parallel Efficiency\n",
+      "PyTensor Default  69.5          6.6          138     475      7       0.10              \n",
+      "PyTensor Numba    95.2          29.8         10      308      3       0.31              \n",
+      "Numpyro           356.8         5419.6       248     1069     3       15.19             \n",
+      "Nutpie Numba      278.0         3117.2       147     830      3       11.21             \n",
+      "====================================================================================================\n",
+      "\n",
+      "Most efficient backend: PyTensor Default with 7 ESS/second\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create timing results using Polars\n",
+    "timing_data = []\n",
+    "for backend_name, metrics in results.items():\n",
+    "    wall_time = metrics.get(\"wall_time\", 0)\n",
+    "    cpu_time = metrics.get(\"cpu_time\", 0)\n",
+    "    min_ess = metrics.get(\"min_ess\", 0)\n",
+    "    mean_ess = metrics.get(\"mean_ess\", 0)\n",
+    "    ess_per_sec = mean_ess / wall_time if wall_time > 0 else 0\n",
+    "    parallel_eff = cpu_time / wall_time if wall_time > 0 else 0\n",
+    "\n",
+    "    timing_data.append(\n",
+    "        {\n",
+    "            \"Sampling Backend\": backend_name,\n",
+    "            \"Wall Time (s)\": wall_time,\n",
+    "            \"CPU Time (s)\": cpu_time,\n",
+    "            \"Min ESS\": min_ess,\n",
+    "            \"Mean ESS\": mean_ess,\n",
+    "            \"ESS/sec\": ess_per_sec,\n",
+    "            \"Parallel Efficiency\": parallel_eff,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "# Create Polars DataFrame and sort by ESS/sec descending\n",
+    "df = pl.DataFrame(timing_data)\n",
+    "df = df.sort(\"ESS/sec\", descending=True)\n",
+    "\n",
+    "print(\"\\nRaw ESS/sec values (for debugging):\")\n",
+    "for row in df.iter_rows(named=True):\n",
+    "    print(f\"{row['Sampling Backend']}: {row['ESS/sec']:.2f}\")\n",
+    "\n",
+    "print(\"\\nPerformance Summary Table:\")\n",
+    "print(\"=\" * 100)\n",
+    "print(\n",
+    "    f\"{'Sampling Backend':<17} {'Wall Time (s)':<13} {'CPU Time (s)':<12} {'Min ESS':<7} {'Mean ESS':<8} {'ESS/sec':<7} {'Parallel Efficiency':<18}\"\n",
+    ")\n",
+    "\n",
+    "for row in df.iter_rows(named=True):\n",
+    "    print(\n",
+    "        f\"{row['Sampling Backend']:<17} {row['Wall Time (s)']:<13.1f} {row['CPU Time (s)']:<12.1f} {row['Min ESS']:<7.0f} {row['Mean ESS']:<8.0f} {row['ESS/sec']:<7.0f} {row['Parallel Efficiency']:<18.2f}\"\n",
+    "    )\n",
+    "\n",
+    "print(\"=\" * 100)\n",
+    "\n",
+    "# Get the best backend (first row after sorting)\n",
+    "best_row = df.row(0, named=True)\n",
+    "best_backend = best_row[\"Sampling Backend\"]\n",
+    "best_ess_per_sec = best_row[\"ESS/sec\"]\n",
+    "print(f\"\\nMost efficient backend: {best_backend} with {best_ess_per_sec:.0f} ESS/second\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_440746/1052382800.py:38: UserWarning: The figure layout has changed to tight\n",
+      "  plt.tight_layout()\n"
+     ]
     },
     {
      "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:09&lt;00:00  Chains in warmup: 0, Divergences: 0]\n",
-       "    </div>\n",
-       "    "
-      ],
+      "image/png": "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",
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 1200x800 with 4 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "image/png": {
+       "height": 788,
+       "width": 1187
+      }
+     },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 8))\n",
+    "\n",
+    "# Convert Polars DataFrame to lists for plotting\n",
+    "backends = df[\"Sampling Backend\"].to_list()\n",
+    "wall_times = df[\"Wall Time (s)\"].to_list()\n",
+    "mean_ess_values = df[\"Mean ESS\"].to_list()\n",
+    "ess_per_sec_values = df[\"ESS/sec\"].to_list()\n",
+    "\n",
+    "ax1.bar(backends, wall_times, color=\"skyblue\")\n",
+    "ax1.set_ylabel(\"Wall Time (seconds)\")\n",
+    "ax1.set_title(\"Sampling Time\")\n",
+    "ax1.tick_params(axis=\"x\", rotation=45)\n",
+    "\n",
+    "ax2.bar(backends, mean_ess_values, color=\"lightgreen\")\n",
+    "ax2.set_ylabel(\"Mean ESS\")\n",
+    "ax2.set_title(\"Effective Sample Size\")\n",
+    "ax2.tick_params(axis=\"x\", rotation=45)\n",
+    "\n",
+    "ax3.bar(backends, ess_per_sec_values, color=\"coral\")\n",
+    "ax3.set_ylabel(\"ESS per Second\")\n",
+    "ax3.set_title(\"Sampling Efficiency\")\n",
+    "ax3.tick_params(axis=\"x\", rotation=45)\n",
+    "\n",
+    "ax4.scatter(wall_times, mean_ess_values, s=200, alpha=0.6)\n",
+    "for i, backend in enumerate(backends):\n",
+    "    ax4.annotate(\n",
+    "        backend,\n",
+    "        (wall_times[i], mean_ess_values[i]),\n",
+    "        xytext=(5, 5),\n",
+    "        textcoords=\"offset points\",\n",
+    "        fontsize=9,\n",
+    "    )\n",
+    "ax4.set_xlabel(\"Wall Time (seconds)\")\n",
+    "ax4.set_ylabel(\"Mean ESS\")\n",
+    "ax4.set_title(\"Time vs. Effective Sample Size\")\n",
+    "ax4.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Special Cases and Advanced Usage\n",
+    "\n",
+    "### Using PyMC's Built-in Sampler with Different Backends\n",
+    "\n",
+    "In certain scenarios, you may need to use PyMC's Python-based sampler while still benefiting from faster computational backends. This situation commonly arises when working with models that contain discrete variables, which require PyMC's specialized sampling algorithms. Even in these cases, you can significantly improve performance by compiling the model's computational graph to more efficient backends.\n",
+    "\n",
+    "The following examples demonstrate how to use PyMC's built-in sampler with different compilation targets. The `fast_run` mode uses optimized C compilation, which provides good performance while maintaining full compatibility. The `numba` mode offers the only way to access Numba's just-in-time compilation benefits when using PyMC's sampler. The `jax` mode enables JAX compilation, though for JAX workflows, Nutpie or NumPyro typically provide better performance.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "NUTS: [ell, eta, f_rotated_, sigma, nu]\n",
+      "/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+      "  self.pid = os.fork()\n",
+      "/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+      "  self.pid = os.fork()\n",
+      "Sampling 2 chains for 500 tune and 500 draw iterations (1_000 + 1_000 draws total) took 49 seconds.\n",
+      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "with gp_latent_model():\n",
+    "    idata_c = pm.sample(\n",
+    "        draws=n_draws,\n",
+    "        tune=n_tune,\n",
+    "        chains=n_chains,\n",
+    "        nuts_sampler=\"pymc\",\n",
+    "        compile_kwargs={\"mode\": \"fast_run\"},\n",
+    "        progressbar=False,\n",
+    "    )\n",
+    "\n",
+    "# with gp_latent_model():\n",
+    "#     idata_pymc_numba = pm.sample(draws=n_draws, tune=n_tune, chains=n_chains, nuts_sampler=\"pymc\", compile_kwargs={\"mode\": \"numba\"}, progressbar=False)\n",
+    "\n",
+    "# with gp_latent_model():\n",
+    "#     idata_pymc_jax = pm.sample(draws=n_draws, tune=n_tune, chains=n_chains, nuts_sampler=\"pymc\", compile_kwargs={\"mode\": \"jax\"}, progressbar=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above examples are commented out to avoid redundant sampling in this demonstration notebook. In practice, you would uncomment and run the configuration that matches your model's requirements. These compilation modes allow you to access faster computational backends even when you must use PyMC's Python-based sampler for compatibility reasons.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Models with Discrete Variables\n",
+    "\n",
+    "When working with models that contain discrete variables, you have no choice but to use PyMC's built-in sampler. This is because discrete variables require specialized sampling algorithms like Slice sampling or Metropolis-Hastings that are only available in PyMC's Python implementation. The example below demonstrates a typical scenario where this constraint applies.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 37.6 s, sys: 3.34 s, total: 41 s\n",
-      "Wall time: 16.1 s\n"
+      "Generated 100 observations with 4 features\n",
+      "True group distribution: [27 33 40]\n",
+      "Outcome distribution: [56 44]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [group_probs, mu_intercept, sigma_intercept, intercepts, mu_slopes, sigma_slopes, slopes]\n",
+      ">CategoricalGibbsMetropolis: [group_assignments]\n",
+      "/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+      "  self.pid = os.fork()\n",
+      "/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+      "  self.pid = os.fork()\n",
+      "Sampling 2 chains for 500 tune and 125 draw iterations (1_000 + 250 draws total) took 6 seconds.\n",
+      "There were 39 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
      ]
     }
    ],
    "source": [
-    "%%time\n",
-    "with PPCA:\n",
-    "    idata_nutpie = pm.sample(nuts_sampler=\"nutpie\")"
+    "# Example: Hierarchical Logistic Regression with Unknown Group Membership\n",
+    "# This is a realistic model where we have binary outcomes but don't know\n",
+    "# which latent group each observation belongs to\n",
+    "\n",
+    "\n",
+    "def generate_group_data(n_obs=200, n_groups=3, n_features=4, random_seed=42):\n",
+    "    \"\"\"Generate synthetic data for hierarchical logistic regression with unknown groups\"\"\"\n",
+    "    rng = np.random.default_rng(random_seed)\n",
+    "\n",
+    "    # True group assignments (unknown to the model)\n",
+    "    true_groups = rng.choice(n_groups, size=n_obs)\n",
+    "\n",
+    "    # Group-specific intercepts and slopes\n",
+    "    true_intercepts = np.array([-1.5, 0.0, 1.2])  # Different baseline rates\n",
+    "    true_slopes = rng.normal(0, 0.8, size=(n_groups, n_features))\n",
+    "\n",
+    "    # Generate features\n",
+    "    X = rng.standard_normal(size=(n_obs, n_features))\n",
+    "\n",
+    "    # Generate outcomes based on true group membership\n",
+    "    y = np.zeros(n_obs, dtype=int)\n",
+    "    for i in range(n_obs):\n",
+    "        group = true_groups[i]\n",
+    "        logit_p = true_intercepts[group] + X[i] @ true_slopes[group]\n",
+    "        p = 1 / (1 + np.exp(-logit_p))\n",
+    "        y[i] = rng.binomial(1, p)\n",
+    "\n",
+    "    return X, y, true_groups\n",
+    "\n",
+    "\n",
+    "# Generate data\n",
+    "X_discrete, y_discrete, true_groups = generate_group_data(n_obs=100, n_groups=3)\n",
+    "n_obs, n_features = X_discrete.shape\n",
+    "n_groups = 3\n",
+    "\n",
+    "print(f\"Generated {n_obs} observations with {n_features} features\")\n",
+    "print(f\"True group distribution: {np.bincount(true_groups)}\")\n",
+    "print(f\"Outcome distribution: {np.bincount(y_discrete)}\")\n",
+    "\n",
+    "# Hierarchical logistic regression with unknown group membership\n",
+    "with pm.Model() as discrete_mixture_model:\n",
+    "    # Group membership probabilities\n",
+    "    group_probs = pm.Dirichlet(\"group_probs\", a=np.ones(n_groups))\n",
+    "\n",
+    "    # Latent group assignments for each observation\n",
+    "    group_assignments = pm.Categorical(\"group_assignments\", p=group_probs, shape=n_obs)\n",
+    "\n",
+    "    # Hierarchical priors for group-specific parameters\n",
+    "    # Group-specific intercepts\n",
+    "    mu_intercept = pm.Normal(\"mu_intercept\", 0, 2)\n",
+    "    sigma_intercept = pm.HalfNormal(\"sigma_intercept\", 1)\n",
+    "    intercepts = pm.Normal(\"intercepts\", mu_intercept, sigma_intercept, shape=n_groups)\n",
+    "\n",
+    "    # Group-specific slopes\n",
+    "    mu_slopes = pm.Normal(\"mu_slopes\", 0, 1, shape=n_features)\n",
+    "    sigma_slopes = pm.HalfNormal(\"sigma_slopes\", 1, shape=n_features)\n",
+    "    slopes = pm.Normal(\"slopes\", mu_slopes, sigma_slopes, shape=(n_groups, n_features))\n",
+    "\n",
+    "    # Linear predictor using group assignments\n",
+    "    # This is where the discrete variables matter!\n",
+    "    linear_pred = intercepts[group_assignments] + pm.math.sum(\n",
+    "        slopes[group_assignments] * X_discrete, axis=1\n",
+    "    )\n",
+    "\n",
+    "    # Likelihood\n",
+    "    y_obs = pm.Bernoulli(\"y_obs\", logit_p=linear_pred, observed=y_discrete)\n",
+    "\n",
+    "    # Sample with compound step (Metropolis for discrete + NUTS for continuous)\n",
+    "    trace_discrete = pm.sample(\n",
+    "        chains=2, draws=125, tune=500, progressbar=False  # Smaller draws since this is more complex\n",
+    "    )"
    ]
   },
   {
@@ -425,52 +951,48 @@
    "metadata": {},
    "source": [
     "## Authors\n",
-    "Authored by Thomas Wiecki in July 2023"
+    "\n",
+    "- Originally authored by Thomas Wiecki in July 2023\n",
+    "- Updated and expanded by Chris Fonnesbeck in May 2025\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Tue Jul 11 2023\n",
+      "Last updated: Sat Jun 14 2025\n",
       "\n",
       "Python implementation: CPython\n",
-      "Python version       : 3.11.4\n",
-      "IPython version      : 8.14.0\n",
+      "Python version       : 3.12.10\n",
+      "IPython version      : 9.2.0\n",
       "\n",
-      "pytensor: 2.12.3\n",
-      "arviz   : 0.15.1\n",
-      "pymc    : 5.6.0\n",
-      "numpyro : 0.12.1\n",
-      "blackjax: 0.9.6\n",
-      "nutpie  : 0.6.0\n",
+      "pytensor: 2.30.3\n",
+      "arviz   : 0.21.0\n",
+      "pymc    : 5.22.0\n",
+      "numpyro : 0.18.0\n",
+      "blackjax: 0.0.0\n",
+      "nutpie  : 0.14.3\n",
       "\n",
-      "numpy     : 1.24.4\n",
-      "pymc      : 5.6.0\n",
-      "matplotlib: 3.7.1\n",
-      "arviz     : 0.15.1\n",
+      "pymc      : 5.22.0\n",
+      "pandas    : 2.2.3\n",
+      "arviz     : 0.21.0\n",
+      "numpyro   : 0.18.0\n",
+      "matplotlib: 3.10.3\n",
+      "numpy     : 2.2.6\n",
       "\n",
-      "Watermark: 2.4.3\n",
+      "Watermark: 2.5.0\n",
       "\n"
      ]
     }
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p pytensor,arviz,pymc,numpyro,blackjax,nutpie"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    ":::{include} ../page_footer.md\n",
-    ":::"
+    "%watermark -n -u -v -iv -w -p pytensor,arviz,pymc,numpnutpie"
    ]
   }
  ],
@@ -487,9 +1009,9 @@
    "id": "f0a28dd06620aa86142931c1f10b5434"
   },
   "kernelspec": {
-   "display_name": "pymc5recent",
+   "display_name": "default",
    "language": "python",
-   "name": "pymc5recent"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -501,7 +1023,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.10"
   },
   "latex_envs": {
    "bibliofile": "biblio.bib",
diff --git a/examples/samplers/fast_sampling_with_jax_and_numba.myst.md b/examples/samplers/fast_sampling_with_jax_and_numba.myst.md
index 3c3c3ede7..4c48f844a 100644
--- a/examples/samplers/fast_sampling_with_jax_and_numba.myst.md
+++ b/examples/samplers/fast_sampling_with_jax_and_numba.myst.md
@@ -5,9 +5,9 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: pymc5recent
+  display_name: default
   language: python
-  name: pymc5recent
+  name: python3
 ---
 
 (faster_sampling_notebook)=
@@ -22,114 +22,561 @@ kernelspec:
 
 +++
 
-PyMC can compile its models to various execution backends through PyTensor, including:
-* C
-* JAX
-* Numba
+PyMC offers multiple sampling backends that can dramatically improve performance depending on your model size and requirements. Each backend has distinct advantages and is optimized for different use cases.
 
-By default, PyMC is using the C backend which then gets called by the Python-based samplers.
+### PyMC's Built-in Sampler
 
-However, by compiling to other backends, we can use samplers written in other languages than Python that call the PyMC model without any Python-overhead.
+```python
+pm.sample()
+```
+
+The default PyMC sampler uses a Python-based NUTS implementation that provides maximum compatibility with all PyMC features. This sampler is required when working with models that contain discrete variables, as it's the only option that works together with other non-gradient based samplers like Slice and Metropolis. While this sampler can compile the underlying model to different backends (C, Numba, PyTensor or JAX) using PyTensor's compilation system via the `compile_kwargs` parameter, it maintains Python overhead that can limit performance, particularly for small models.
+
+### Nutpie Sampler
+
+```python
+pm.sample(nuts_sampler="nutpie", nuts_sampler_kwargs={"backend": "numba"})
+pm.sample(nuts_sampler="nutpie", nuts_sampler_kwargs={"backend": "jax"})
+pm.sample(nuts_sampler="nutpie", nuts_sampler_kwargs={"backend": "jax", "gradient_backend": "pytensor"})
+```
+
+Nutpie is PyMC's cutting-edge performance sampler. Written in Rust, it eliminates Python overhead and provides exceptional performance for continuous models. In addition, it has an improved NUTS adaptation algorithm that generalizes mass matrix adaptation from affine functions to arbitrary diffeomorphisms. This helps to identify transformations that adapt to the posterior’s scale and shape. The Numba backend typically offers the highest performance for most use cases, while the JAX backend excels with very large models and provides GPU acceleration capabilities. Nutpie is particularly well-suited for production workflows where sampling speed is critical.
+
+### NumPyro Sampler
+
+```python
+pm.sample(nuts_sampler="numpyro", nuts_sampler_kwargs={"chain_method": "parallel"})
+# GPU-accelerated
+pm.sample(nuts_sampler="numpyro", nuts_sampler_kwargs={"chain_method": "vectorized"})
+```
+
+NumPyro provides a mature JAX-based sampling implementation that integrates seamlessly with the broader JAX ecosystem. This sampler benefits from years of development within the JAX community and provides reliable performance characteristics, with excellent GPU support for accelerated computation.
+
+### BlackJAX Sampler
+
+```python
+pm.sample(nuts_sampler="blackjax")
+```
+
+BlackJAX offers another JAX-based sampling implementation focused on flexibility and research applications. While it provides similar capabilities to NumPyro, it's less commonly used in production environments. BlackJAX can be valuable for experimental workflows or when specific JAX-based features are required.
+
++++
+
+## Installation Requirements
+
+To use the various sampling backends, you need to install the corresponding packages. Nutpie is the recommended high-performance option and can be installed with pip or conda/mamba (e.g. `conda install nutpie`). For JAX-based workflows, NumPyro provides mature functionality and is installed with the `numpyro` package. BlackJAX offers an alternative JAX implementation and is available in the `blackjax` package.
 
-For the JAX backend there is the NumPyro and BlackJAX NUTS sampler available. To use these samplers, you have to install `numpyro` and `blackjax`. Both of them are available through conda/mamba: `mamba install -c conda-forge numpyro blackjax`.
++++
+
+## Performance Guidelines
+
+Understanding when to use each sampler depends on several key factors including model size, variable types, and computational requirements.
+
+For **small models**, NumPyro typically provides the best balance of performance and reliability. The compilation overhead is minimal, and its mature JAX implementation handles these models efficiently. **Large models** generally perform best with Nutpie's Numba backend for consistent CPU performance or Nutpie's JAX backend when GPU acceleration is needed or memory efficiency is critical.
+
+Models containing **discrete variables** must use PyMC's built-in sampler, as it's the only implementation that supports compatible (_i.e._, non-gradient based) sampling algorithms. For purely continuous models, all sampling backends are available, making performance the primary consideration.
 
-For the Numba backend, there is the [Nutpie sampler](https://github.com/pymc-devs/nutpie) written in Rust. To use this sampler you need `nutpie` installed: `mamba install -c conda-forge nutpie`. 
+**Numba** excels at CPU optimization and provides consistent performance across different model types. It's particularly effective for models with complex mathematical operations that benefit from just-in-time compilation. **JAX** offers superior performance for very large models and provides natural GPU acceleration, making it ideal when computational resources are a limiting factor. The **C** backend serves as a reliable fallback option with broad compatibility but typically offers lower performance than the alternatives.
 
 ```{code-cell} ipython3
+import os
+import time
+
+from collections import defaultdict
+
 import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
+import polars as pl
 import pymc as pm
 
-rng = np.random.default_rng(seed=42)
+os.environ["XLA_FLAGS"] = "--xla_force_host_platform_device_count=4"
+
+%config InlineBackend.figure_format = 'retina'
+az.style.use("arviz-darkgrid")
+
+# rng = np.random.default_rng(seed=42)
 print(f"Running on PyMC v{pm.__version__}")
 ```
 
 ```{code-cell} ipython3
-%config InlineBackend.figure_format = 'retina'
-az.style.use("arviz-darkgrid")
+# Dictionary to store all results
+results = defaultdict(dict)
+
+
+class TimingContext:
+    def __init__(self, name):
+        self.name = name
+
+    def __enter__(self):
+        self.start_wall = time.perf_counter()
+        self.start_cpu = time.process_time()
+        return self
+
+    def __exit__(self, *args):
+        self.end_wall = time.perf_counter()
+        self.end_cpu = time.process_time()
+
+        wall_time = self.end_wall - self.start_wall
+        cpu_time = self.end_cpu - self.start_cpu
+
+        results[self.name]["wall_time"] = wall_time
+        results[self.name]["cpu_time"] = cpu_time
+
+        print(f"Wall time: {wall_time:.1f} s")
+        print(f"CPU time: {cpu_time:.1f} s")
+```
+
+```{code-cell} ipython3
+def build_gp_latent_dataset(n=200, random_seed=42):
+    """
+    Generate data from a Gaussian Process with Student-T distributed noise.
+
+    This creates a challenging latent variable problem that tests the samplers'
+    ability to efficiently explore the high-dimensional posterior over the
+    latent GP function values.
+    """
+    rng_local = np.random.default_rng(random_seed)
+
+    # Input locations
+    X = np.linspace(0, 10, n)[:, None]
+
+    # True GP hyperparameters
+    ell_true = 1.0  # lengthscale
+    eta_true = 4.0  # scale
+
+    # Create true covariance function and sample from GP prior
+    cov_func = eta_true**2 * pm.gp.cov.ExpQuad(1, ell_true)
+    mean_func = pm.gp.mean.Zero()
+
+    # Sample latent function values from GP prior with jitter for numerical stability
+    K = cov_func(X).eval()
+    # Add jitter to diagonal for numerical stability
+    K += 1e-6 * np.eye(n)
+
+    f_true = pm.draw(pm.MvNormal.dist(mu=mean_func(X), cov=K), 1, random_seed=rng_local)
+
+    # Add Student-T distributed noise (heavier tails than Gaussian)
+    sigma_true = 1.0
+    nu_true = 5.0  # degrees of freedom
+    y = f_true + sigma_true * rng_local.standard_t(df=nu_true, size=n)
+
+    print(f"Generated GP data with {n} points")
+    print(f"True hyperparameters: lengthscale={ell_true}, scale={eta_true}")
+    print(f"Noise: σ={sigma_true}, ν={nu_true} (Student-T)")
+
+    return X, y, f_true
+
+
+# Generate the challenging GP dataset
+N = 100  # number of data points
+X, y_obs, f_true = build_gp_latent_dataset(N)
 ```
 
-We will use a simple probabilistic PCA model as our example.
+## The Challenge: Latent Gaussian Process Regression
+
+To properly evaluate the performance differences between sampling backends, we need a model that presents genuine computational challenges. Our test case is a **latent Gaussian Process (GP) regression** with Student-T distributed noise—a model that creates several difficulties for MCMC samplers:
+
+### Why This Model Is Challenging
+
+1. **High-dimensional latent space**: The model includes 200 latent function values as parameters, creating a high-dimensional posterior to explore.
+
+2. **Complex posterior correlations**: The GP prior induces strong correlations between nearby function values through the covariance matrix, making the posterior geometry complex.
+
+3. **Non-Gaussian likelihood**: The Student-T likelihood has heavier tails than Gaussian noise, requiring robust sampling of outlier-sensitive parameters.
+
+4. **Hierarchical structure**: The model includes hyperparameters (lengthscale, scale, noise parameters) that control the GP behavior, creating additional dependencies.
+
+5. **Computational intensity**: Each likelihood evaluation requires computing with a 200×200 covariance matrix, making efficient linear algebra crucial.
+
+This combination creates a realistic test case where different sampling backends' strengths and weaknesses become apparent. The model is representative of many real-world applications in machine learning, spatial statistics, and time series analysis.
+
+### Model Structure
+
+Our latent GP model places a Gaussian Process prior on an unknown function f(x), then observes noisy measurements:
+
+- **GP prior**: f(x) ~ GP(0, k(x,x')) with squared exponential covariance
+- **Hyperpriors**: Lengthscale ~ Gamma(2,1), Scale ~ HalfNormal(5)  
+- **Noise model**: y ~ StudentT(f(x), σ, ν) with σ ~ HalfNormal(2), ν ~ 1+Gamma(2,0.1)
+
+The latent function values f are sampled directly (not marginalized), creating the computational challenge that distinguishes different sampling backends.
 
 ```{code-cell} ipython3
-def build_toy_dataset(N, D, K, sigma=1):
-    x_train = np.zeros((D, N))
-    w = rng.normal(
-        0.0,
-        2.0,
-        size=(D, K),
-    )
-    z = rng.normal(0.0, 1.0, size=(K, N))
-    mean = np.dot(w, z)
-    for d in range(D):
-        for n in range(N):
-            x_train[d, n] = rng.normal(mean[d, n], sigma)
+def gp_latent_model():
 
-    print("True principal axes:")
-    print(w)
-    return x_train
+    with pm.Model() as model:
+        ell = pm.Gamma("ell", alpha=2, beta=1)
+        eta = pm.HalfNormal("eta", sigma=5)
 
+        cov = eta**2 * pm.gp.cov.ExpQuad(1, ell)
+        gp = pm.gp.Latent(cov_func=cov)
 
-N = 5000  # number of data points
-D = 2  # data dimensionality
-K = 1  # latent dimensionality
+        f = gp.prior("f", X=X)
 
-data = build_toy_dataset(N, D, K)
+        sigma = pm.HalfNormal("sigma", sigma=2.0)
+        nu = 1 + pm.Gamma("nu", alpha=2, beta=0.1)
+
+        _ = pm.StudentT("y", mu=f, lam=1.0 / sigma, nu=nu, observed=y_obs)
+    return model
 ```
 
+## Performance Comparison
+
+Now let's compare the performance of different sampling backends on our challenging latent GP model. We'll measure sampling speed and efficiency, in terms of effective samples drawn.
+
+### 1. PyTensor Default Sampler
+
 ```{code-cell} ipython3
-plt.scatter(data[0, :], data[1, :], color="blue", alpha=0.1)
-plt.axis([-10, 10, -10, 10])
-plt.title("Simulated data set")
+n_draws = 250
+n_tune = 1000
+n_chains = 4
+
+model = gp_latent_model()
+with TimingContext("PyTensor Default"):
+    with model:
+        idata_pytensor_default = pm.sample(
+            draws=n_draws, tune=n_tune, chains=n_chains, progressbar=False
+        )
+
+ess_pytensor_default = az.ess(idata_pytensor_default)
+min_ess = min([ess_pytensor_default[var].values.min() for var in ess_pytensor_default.data_vars])
+mean_ess = np.mean(
+    [ess_pytensor_default[var].values.mean() for var in ess_pytensor_default.data_vars]
+)
+results["PyTensor Default"]["min_ess"] = min_ess
+results["PyTensor Default"]["mean_ess"] = mean_ess
+print(f"Min ESS: {min_ess:.0f}, Mean ESS: {mean_ess:.0f}")
 ```
 
+### 2. PyTensor Sampler with Numba Backend
+
+```{code-cell} ipython3
+n_draws = 250
+n_tune = 1000
+n_chains = 4
+
+model = gp_latent_model()
+with TimingContext("PyTensor Numba"):
+    with model:
+        idata_pytensor_numba = pm.sample(
+            draws=n_draws,
+            tune=n_tune,
+            chains=n_chains,
+            compile_kwargs={"mode": "numba"},
+            progressbar=False,
+        )
+
+ess_pytensor_numba = az.ess(idata_pytensor_numba)
+min_ess = min([ess_pytensor_numba[var].values.min() for var in ess_pytensor_numba.data_vars])
+mean_ess = np.mean([ess_pytensor_numba[var].values.mean() for var in ess_pytensor_numba.data_vars])
+results["PyTensor Numba"]["min_ess"] = min_ess
+results["PyTensor Numba"]["mean_ess"] = mean_ess
+print(f"Min ESS: {min_ess:.0f}, Mean ESS: {mean_ess:.0f}")
+```
+
+### 3. PyTensor with PyTorch Backend
+
+```{code-cell} ipython3
+n_draws = 250
+n_tune = 1000
+n_chains = 4
+
+model = gp_latent_model()
+with TimingContext("PyTensor PyTorch"):
+    with model:
+        idata_pytensor_pytorch = pm.sample(
+            draws=n_draws,
+            tune=n_tune,
+            chains=n_chains,
+            compile_kwargs={"mode": "pytorch"},
+            progressbar=False,
+        )
+
+ess_pytensor_pytorch = az.ess(idata_pytensor_pytorch)
+min_ess = min([ess_pytensor_pytorch[var].values.min() for var in ess_pytensor_pytorch.data_vars])
+mean_ess = np.mean(
+    [ess_pytensor_pytorch[var].values.mean() for var in ess_pytensor_pytorch.data_vars]
+)
+results["PyTensor PyTorch"]["min_ess"] = min_ess
+results["PyTensor PyTorch"]["mean_ess"] = mean_ess
+print(f"Min ESS: {min_ess:.0f}, Mean ESS: {mean_ess:.0f}")
+```
+
+### 4. Nutpie Sampler with Numba Backend
+
 ```{code-cell} ipython3
-with pm.Model() as PPCA:
-    w = pm.Normal("w", mu=0, sigma=2, shape=[D, K], transform=pm.distributions.transforms.Ordered())
-    z = pm.Normal("z", mu=0, sigma=1, shape=[N, K])
-    x = pm.Normal("x", mu=w.dot(z.T), sigma=1, shape=[D, N], observed=data)
+model = gp_latent_model()
+with TimingContext("Nutpie Numba"):
+    with model:
+        idata_nutpie_numba = pm.sample(
+            draws=n_draws,
+            tune=n_tune,
+            chains=n_chains,
+            nuts_sampler="nutpie",
+            nuts_sampler_kwargs={"backend": "numba"},
+            progressbar=False,
+        )
+
+ess_nutpie_numba = az.ess(idata_nutpie_numba)
+min_ess = min([ess_nutpie_numba[var].values.min() for var in ess_nutpie_numba.data_vars])
+mean_ess = np.mean([ess_nutpie_numba[var].values.mean() for var in ess_nutpie_numba.data_vars])
+results["Nutpie Numba"]["min_ess"] = min_ess
+results["Nutpie Numba"]["mean_ess"] = mean_ess
+print(f"Min ESS: {min_ess:.0f}, Mean ESS: {mean_ess:.0f}")
 ```
 
-## Sampling using Python NUTS sampler
+### 5. Nutpie Sampler with JAX Backend
 
 ```{code-cell} ipython3
-%%time
-with PPCA:
-    idata_pymc = pm.sample()
+model = gp_latent_model()
+with TimingContext("Nutpie JAX"):
+    with model:
+        idata_nutpie_jax = pm.sample(
+            draws=n_draws,
+            tune=n_tune,
+            chains=n_chains,
+            nuts_sampler="nutpie",
+            nuts_sampler_kwargs={"backend": "jax"},
+            progressbar=False,
+        )
+
+ess_nutpie_jax = az.ess(idata_nutpie_jax)
+min_ess = min([ess_nutpie_jax[var].values.min() for var in ess_nutpie_jax.data_vars])
+mean_ess = np.mean([ess_nutpie_jax[var].values.mean() for var in ess_nutpie_jax.data_vars])
+results["Nutpie JAX"]["min_ess"] = min_ess
+results["Nutpie JAX"]["mean_ess"] = mean_ess
+print(f"Min ESS: {min_ess:.0f}, Mean ESS: {mean_ess:.0f}")
 ```
 
-## Sampling using NumPyro JAX NUTS sampler
+### 6. NumPyro Sampler
 
 ```{code-cell} ipython3
-%%time
-with PPCA:
-    idata_numpyro = pm.sample(nuts_sampler="numpyro", progressbar=False)
+model = gp_latent_model()
+with TimingContext("Numpyro"):
+    with model:
+        idata_numpyro = pm.sample(
+            draws=n_draws,
+            tune=n_tune,
+            chains=n_chains,
+            nuts_sampler="numpyro",
+            nuts_sampler_kwargs={"chain_method": "parallel"},
+            progressbar=False,
+        )
+
+ess_numpyro = az.ess(idata_numpyro)
+min_ess = min([ess_numpyro[var].values.min() for var in ess_numpyro.data_vars])
+mean_ess = np.mean([ess_numpyro[var].values.mean() for var in ess_numpyro.data_vars])
+results["Numpyro"]["min_ess"] = min_ess
+results["Numpyro"]["mean_ess"] = mean_ess
+```
+
+```{code-cell} ipython3
+# Create timing results using Polars
+timing_data = []
+for backend_name, metrics in results.items():
+    wall_time = metrics.get("wall_time", 0)
+    cpu_time = metrics.get("cpu_time", 0)
+    min_ess = metrics.get("min_ess", 0)
+    mean_ess = metrics.get("mean_ess", 0)
+    ess_per_sec = mean_ess / wall_time if wall_time > 0 else 0
+    parallel_eff = cpu_time / wall_time if wall_time > 0 else 0
+
+    timing_data.append(
+        {
+            "Sampling Backend": backend_name,
+            "Wall Time (s)": wall_time,
+            "CPU Time (s)": cpu_time,
+            "Min ESS": min_ess,
+            "Mean ESS": mean_ess,
+            "ESS/sec": ess_per_sec,
+            "Parallel Efficiency": parallel_eff,
+        }
+    )
+
+# Create Polars DataFrame and sort by ESS/sec descending
+df = pl.DataFrame(timing_data)
+df = df.sort("ESS/sec", descending=True)
+
+print("\nRaw ESS/sec values (for debugging):")
+for row in df.iter_rows(named=True):
+    print(f"{row['Sampling Backend']}: {row['ESS/sec']:.2f}")
+
+print("\nPerformance Summary Table:")
+print("=" * 100)
+print(
+    f"{'Sampling Backend':<17} {'Wall Time (s)':<13} {'CPU Time (s)':<12} {'Min ESS':<7} {'Mean ESS':<8} {'ESS/sec':<7} {'Parallel Efficiency':<18}"
+)
+
+for row in df.iter_rows(named=True):
+    print(
+        f"{row['Sampling Backend']:<17} {row['Wall Time (s)']:<13.1f} {row['CPU Time (s)']:<12.1f} {row['Min ESS']:<7.0f} {row['Mean ESS']:<8.0f} {row['ESS/sec']:<7.0f} {row['Parallel Efficiency']:<18.2f}"
+    )
+
+print("=" * 100)
+
+# Get the best backend (first row after sorting)
+best_row = df.row(0, named=True)
+best_backend = best_row["Sampling Backend"]
+best_ess_per_sec = best_row["ESS/sec"]
+print(f"\nMost efficient backend: {best_backend} with {best_ess_per_sec:.0f} ESS/second")
+```
+
+```{code-cell} ipython3
+fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 8))
+
+# Convert Polars DataFrame to lists for plotting
+backends = df["Sampling Backend"].to_list()
+wall_times = df["Wall Time (s)"].to_list()
+mean_ess_values = df["Mean ESS"].to_list()
+ess_per_sec_values = df["ESS/sec"].to_list()
+
+ax1.bar(backends, wall_times, color="skyblue")
+ax1.set_ylabel("Wall Time (seconds)")
+ax1.set_title("Sampling Time")
+ax1.tick_params(axis="x", rotation=45)
+
+ax2.bar(backends, mean_ess_values, color="lightgreen")
+ax2.set_ylabel("Mean ESS")
+ax2.set_title("Effective Sample Size")
+ax2.tick_params(axis="x", rotation=45)
+
+ax3.bar(backends, ess_per_sec_values, color="coral")
+ax3.set_ylabel("ESS per Second")
+ax3.set_title("Sampling Efficiency")
+ax3.tick_params(axis="x", rotation=45)
+
+ax4.scatter(wall_times, mean_ess_values, s=200, alpha=0.6)
+for i, backend in enumerate(backends):
+    ax4.annotate(
+        backend,
+        (wall_times[i], mean_ess_values[i]),
+        xytext=(5, 5),
+        textcoords="offset points",
+        fontsize=9,
+    )
+ax4.set_xlabel("Wall Time (seconds)")
+ax4.set_ylabel("Mean ESS")
+ax4.set_title("Time vs. Effective Sample Size")
+ax4.grid(True, alpha=0.3)
+
+plt.tight_layout()
+plt.show()
 ```
 
-## Sampling using BlackJAX NUTS sampler
+## Special Cases and Advanced Usage
+
+### Using PyMC's Built-in Sampler with Different Backends
+
+In certain scenarios, you may need to use PyMC's Python-based sampler while still benefiting from faster computational backends. This situation commonly arises when working with models that contain discrete variables, which require PyMC's specialized sampling algorithms. Even in these cases, you can significantly improve performance by compiling the model's computational graph to more efficient backends.
+
+The following examples demonstrate how to use PyMC's built-in sampler with different compilation targets. The `fast_run` mode uses optimized C compilation, which provides good performance while maintaining full compatibility. The `numba` mode offers the only way to access Numba's just-in-time compilation benefits when using PyMC's sampler. The `jax` mode enables JAX compilation, though for JAX workflows, Nutpie or NumPyro typically provide better performance.
 
 ```{code-cell} ipython3
-%%time
-with PPCA:
-    idata_blackjax = pm.sample(nuts_sampler="blackjax")
+with gp_latent_model():
+    idata_c = pm.sample(
+        draws=n_draws,
+        tune=n_tune,
+        chains=n_chains,
+        nuts_sampler="pymc",
+        compile_kwargs={"mode": "fast_run"},
+        progressbar=False,
+    )
+
+# with gp_latent_model():
+#     idata_pymc_numba = pm.sample(draws=n_draws, tune=n_tune, chains=n_chains, nuts_sampler="pymc", compile_kwargs={"mode": "numba"}, progressbar=False)
+
+# with gp_latent_model():
+#     idata_pymc_jax = pm.sample(draws=n_draws, tune=n_tune, chains=n_chains, nuts_sampler="pymc", compile_kwargs={"mode": "jax"}, progressbar=False)
 ```
 
-## Sampling using Nutpie Rust NUTS sampler
+The above examples are commented out to avoid redundant sampling in this demonstration notebook. In practice, you would uncomment and run the configuration that matches your model's requirements. These compilation modes allow you to access faster computational backends even when you must use PyMC's Python-based sampler for compatibility reasons.
+
++++
+
+### Models with Discrete Variables
+
+When working with models that contain discrete variables, you have no choice but to use PyMC's built-in sampler. This is because discrete variables require specialized sampling algorithms like Slice sampling or Metropolis-Hastings that are only available in PyMC's Python implementation. The example below demonstrates a typical scenario where this constraint applies.
 
 ```{code-cell} ipython3
-%%time
-with PPCA:
-    idata_nutpie = pm.sample(nuts_sampler="nutpie")
+# Example: Hierarchical Logistic Regression with Unknown Group Membership
+# This is a realistic model where we have binary outcomes but don't know
+# which latent group each observation belongs to
+
+
+def generate_group_data(n_obs=200, n_groups=3, n_features=4, random_seed=42):
+    """Generate synthetic data for hierarchical logistic regression with unknown groups"""
+    rng = np.random.default_rng(random_seed)
+
+    # True group assignments (unknown to the model)
+    true_groups = rng.choice(n_groups, size=n_obs)
+
+    # Group-specific intercepts and slopes
+    true_intercepts = np.array([-1.5, 0.0, 1.2])  # Different baseline rates
+    true_slopes = rng.normal(0, 0.8, size=(n_groups, n_features))
+
+    # Generate features
+    X = rng.standard_normal(size=(n_obs, n_features))
+
+    # Generate outcomes based on true group membership
+    y = np.zeros(n_obs, dtype=int)
+    for i in range(n_obs):
+        group = true_groups[i]
+        logit_p = true_intercepts[group] + X[i] @ true_slopes[group]
+        p = 1 / (1 + np.exp(-logit_p))
+        y[i] = rng.binomial(1, p)
+
+    return X, y, true_groups
+
+
+# Generate data
+X_discrete, y_discrete, true_groups = generate_group_data(n_obs=100, n_groups=3)
+n_obs, n_features = X_discrete.shape
+n_groups = 3
+
+print(f"Generated {n_obs} observations with {n_features} features")
+print(f"True group distribution: {np.bincount(true_groups)}")
+print(f"Outcome distribution: {np.bincount(y_discrete)}")
+
+# Hierarchical logistic regression with unknown group membership
+with pm.Model() as discrete_mixture_model:
+    # Group membership probabilities
+    group_probs = pm.Dirichlet("group_probs", a=np.ones(n_groups))
+
+    # Latent group assignments for each observation
+    group_assignments = pm.Categorical("group_assignments", p=group_probs, shape=n_obs)
+
+    # Hierarchical priors for group-specific parameters
+    # Group-specific intercepts
+    mu_intercept = pm.Normal("mu_intercept", 0, 2)
+    sigma_intercept = pm.HalfNormal("sigma_intercept", 1)
+    intercepts = pm.Normal("intercepts", mu_intercept, sigma_intercept, shape=n_groups)
+
+    # Group-specific slopes
+    mu_slopes = pm.Normal("mu_slopes", 0, 1, shape=n_features)
+    sigma_slopes = pm.HalfNormal("sigma_slopes", 1, shape=n_features)
+    slopes = pm.Normal("slopes", mu_slopes, sigma_slopes, shape=(n_groups, n_features))
+
+    # Linear predictor using group assignments
+    # This is where the discrete variables matter!
+    linear_pred = intercepts[group_assignments] + pm.math.sum(
+        slopes[group_assignments] * X_discrete, axis=1
+    )
+
+    # Likelihood
+    y_obs = pm.Bernoulli("y_obs", logit_p=linear_pred, observed=y_discrete)
+
+    # Sample with compound step (Metropolis for discrete + NUTS for continuous)
+    trace_discrete = pm.sample(
+        chains=2, draws=125, tune=500, progressbar=False  # Smaller draws since this is more complex
+    )
 ```
 
 ## Authors
-Authored by Thomas Wiecki in July 2023
+
+- Originally authored by Thomas Wiecki in July 2023
+- Updated and expanded by Chris Fonnesbeck in May 2025
 
 ```{code-cell} ipython3
 %load_ext watermark
-%watermark -n -u -v -iv -w -p pytensor,arviz,pymc,numpyro,blackjax,nutpie
+%watermark -n -u -v -iv -w -p pytensor,arviz,pymc,numpnutpie
 ```
-
-:::{include} ../page_footer.md
-:::
diff --git a/pixi.toml b/pixi.toml
index 40a3c032c..a102997ad 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -1,7 +1,6 @@
-[project]
+[workspace]
 authors = ["Chris Fonnesbeck <fonnesbeck@gmail.com>"]
 channels = ["conda-forge"]
-description = "Add a short description here"
 name = "pymc-examples"
 platforms = ["linux-64"]
 version = "0.1.0"
@@ -9,27 +8,17 @@ version = "0.1.0"
 [tasks]
 
 [dependencies]
-python = ">=3.12.5,<4"
-pymc = ">=5.16.2,<6"
-jupyter = ">=1.1.1,<2"
+pymc = ">=5.22.0,<6"
+nutpie = ">=0.14.3,<0.15"
+numpyro = ">=0.18.0,<0.19"
+numba = ">=0.61.2,<0.62"
+ipywidgets = ">=8.1.7,<9"
+arviz = ">=0.21.0,<0.22"
+matplotlib = ">=3.10.3,<4"
+python = ">=3.12.10,<3.13"
 ipykernel = ">=6.29.5,<7"
-ipywidgets = ">=8.1.5,<9"
-numpy = ">=1.26.4,<2"
-arviz = ">=0.19.0,<0.20"
-numpyro = ">=0.15.2,<0.16"
-seaborn = ">=0.13.2,<0.14"
-matplotlib = ">=3.9.2,<4"
-pandas = ">=2.2.2,<3"
-polars = ">=1.6.0,<2"
-esbonio = ">=0.16.4,<0.17"
+blackjax = ">=1.2.4,<2"
 watermark = ">=2.5.0,<3"
-nutpie = ">=0.13.2,<0.14"
-numba = ">=0.60.0,<0.61"
-scikit-learn = ">=1.5.2,<2"
-blackjax = ">=1.2.3,<2"
-networkx = ">=3.4.2,<4"
-bokeh = ">=3.7.2,<4"
-
-[pypi-dependencies]
-pymc-experimental = ">=0.1.2, <0.2"
-pymc-extras = ">=0.2.0, <0.3"
+polars = ">=1.30.0,<2"
+pytorch = ">=2.7.0,<3"
+openblas = ">=0.3.29,<0.4"