diff --git a/demo/bayesian-inference/_test_e2e.py b/demo/bayesian-inference/_test_e2e.py
new file mode 100644
index 000000000..155a85f9f
--- /dev/null
+++ b/demo/bayesian-inference/_test_e2e.py
@@ -0,0 +1,198 @@
+#!/usr/bin/env python
+"""End-to-end test of the Bayesian inference demo notebook logic."""
+
+import time
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import numpyro
+import numpyro.distributions as dist
+from numpyro.infer import MCMC, NUTS, SA
+from tesseract_jax import apply_tesseract
+
+from tesseract_core import Tesseract
+
+# Reduce sample counts for a faster test
+NUM_WARMUP = 100
+NUM_SAMPLES = 200
+
+# ── Step 1: Serve the JAX Lorenz Tesseract ──────────────────────────────
+print("=== Step 1: Serving JAX Lorenz Tesseract ===")
+lorenz = Tesseract.from_image("lorenz-bayesian")
+lorenz.serve()
+print(f"Available endpoints: {lorenz.available_endpoints}")
+
+# ── Step 2: Generate synthetic observations ─────────────────────────────
+print("\n=== Step 2: Generating synthetic observations ===")
+data = np.load("lorenz96_two_scale_F_18_sample_0_small.npz")
+X_states = data["X_states"]
+true_trajectory = X_states[500:]
+X0 = true_trajectory[0]
+x0_jax = jnp.array(X0, dtype=jnp.float32)
+
+OBS_GAP = 10
+N_OBS = 3
+STD_OBS = 0.5
+TRUE_F = 18.0
+N_STEPS = OBS_GAP * N_OBS
+
+# Generate self-consistent observations from the model itself
+true_result = apply_tesseract(
+    lorenz,
+    {"state": x0_jax, "F": jnp.float32(TRUE_F), "dt": 0.005, "n_steps": N_STEPS},
+)
+true_traj = true_result["result"]
+obs_indices = jnp.arange(OBS_GAP - 1, N_STEPS, OBS_GAP)
+true_obs = true_traj[obs_indices]
+
+key = jax.random.PRNGKey(42)
+observations = true_obs + STD_OBS * jax.random.normal(key, true_obs.shape)
+print(f"Observations shape: {observations.shape}")
+
+# ── Step 3: Test apply_tesseract works ──────────────────────────────────
+print("\n=== Step 3: Testing apply_tesseract ===")
+test_result = apply_tesseract(
+    lorenz,
+    {"state": x0_jax, "F": jnp.float32(TRUE_F), "dt": 0.005, "n_steps": N_STEPS},
+)
+print(f"apply_tesseract output keys: {list(test_result.keys())}")
+print(f"result shape: {test_result['result'].shape}")
+
+# ── Step 4: Test jax.grad flows through ─────────────────────────────────
+print("\n=== Step 4: Testing jax.grad through Tesseract ===")
+
+
+def loss_fn(F_val):
+    result = apply_tesseract(
+        lorenz,
+        {"state": x0_jax, "F": F_val, "dt": 0.005, "n_steps": OBS_GAP},
+    )
+    return jnp.sum(result["result"] ** 2)
+
+
+grad_F = jax.grad(loss_fn)(jnp.float32(18.0))
+print(f"grad w.r.t. F: {grad_F}")
+assert not jnp.isnan(grad_F), "Gradient is NaN!"
+print("Gradient OK")
+
+# ── Step 5: Define NumPyro model and run NUTS ───────────────────────────
+print("\n=== Step 5: Running NUTS ===")
+
+
+def bayesian_lorenz_model(observations, x0, obs_gap, n_obs, std_obs):
+    F = numpyro.sample("F", dist.Normal(15.0, 5.0))
+    result = apply_tesseract(
+        lorenz,
+        {"state": x0, "F": F, "dt": 0.005, "n_steps": obs_gap * n_obs},
+    )
+    trajectory = result["result"]
+    obs_idx = jnp.arange(obs_gap - 1, obs_gap * n_obs, obs_gap)
+    predicted_obs = trajectory[obs_idx]
+    numpyro.sample("obs", dist.Normal(predicted_obs, std_obs), obs=observations)
+
+
+nuts_kernel = NUTS(bayesian_lorenz_model)
+mcmc_nuts = MCMC(
+    nuts_kernel, num_warmup=NUM_WARMUP, num_samples=NUM_SAMPLES, num_chains=1
+)
+
+start = time.time()
+mcmc_nuts.run(
+    jax.random.PRNGKey(0),
+    observations=observations,
+    x0=x0_jax,
+    obs_gap=OBS_GAP,
+    n_obs=N_OBS,
+    std_obs=STD_OBS,
+)
+nuts_time = time.time() - start
+mcmc_nuts.print_summary()
+
+nuts_samples = mcmc_nuts.get_samples()
+F_mean = float(nuts_samples["F"].mean())
+F_std = float(nuts_samples["F"].std())
+print(
+    f"\nNUTS: F = {F_mean:.2f} ± {F_std:.2f} (true: {TRUE_F}), time: {nuts_time:.1f}s"
+)
+assert abs(F_mean - TRUE_F) < 5.0, f"NUTS posterior mean too far from truth: {F_mean}"
+print("NUTS posterior check PASSED")
+
+# ── Step 6: Test finite-diff Tesseract ──────────────────────────────────
+print("\n=== Step 6: Serving finite-diff Lorenz Tesseract ===")
+lorenz_fd = Tesseract.from_image("lorenz-finitediff")
+lorenz_fd.serve()
+print(f"Available endpoints: {lorenz_fd.available_endpoints}")
+
+
+def bayesian_lorenz_model_fd(observations, x0, obs_gap, n_obs, std_obs):
+    F = numpyro.sample("F", dist.Normal(15.0, 5.0))
+    result = apply_tesseract(
+        lorenz_fd,
+        {"state": x0, "F": F, "dt": 0.005, "n_steps": obs_gap * n_obs},
+    )
+    trajectory = result["result"]
+    obs_idx = jnp.arange(obs_gap - 1, obs_gap * n_obs, obs_gap)
+    predicted_obs = trajectory[obs_idx]
+    numpyro.sample("obs", dist.Normal(predicted_obs, std_obs), obs=observations)
+
+
+nuts_fd_kernel = NUTS(bayesian_lorenz_model_fd)
+mcmc_nuts_fd = MCMC(
+    nuts_fd_kernel, num_warmup=NUM_WARMUP, num_samples=NUM_SAMPLES, num_chains=1
+)
+
+start = time.time()
+mcmc_nuts_fd.run(
+    jax.random.PRNGKey(0),
+    observations=observations,
+    x0=x0_jax,
+    obs_gap=OBS_GAP,
+    n_obs=N_OBS,
+    std_obs=STD_OBS,
+)
+nuts_fd_time = time.time() - start
+mcmc_nuts_fd.print_summary()
+
+fd_samples = mcmc_nuts_fd.get_samples()
+F_mean_fd = float(fd_samples["F"].mean())
+print(
+    f"\nNUTS (FD): F = {F_mean_fd:.2f} ± {float(fd_samples['F'].std()):.2f} (true: {TRUE_F}), time: {nuts_fd_time:.1f}s"
+)
+assert abs(F_mean_fd - TRUE_F) < 5.0, (
+    f"NUTS-FD posterior mean too far from truth: {F_mean_fd}"
+)
+print("NUTS-FD posterior check PASSED")
+
+# ── Step 7: Gradient-free baseline ──────────────────────────────────────
+print("\n=== Step 7: Running SA (gradient-free) ===")
+sa_kernel = SA(bayesian_lorenz_model)
+mcmc_sa = MCMC(
+    sa_kernel, num_warmup=NUM_WARMUP * 5, num_samples=NUM_SAMPLES, num_chains=1
+)
+
+start = time.time()
+mcmc_sa.run(
+    jax.random.PRNGKey(0),
+    observations=observations,
+    x0=x0_jax,
+    obs_gap=OBS_GAP,
+    n_obs=N_OBS,
+    std_obs=STD_OBS,
+)
+sa_time = time.time() - start
+mcmc_sa.print_summary()
+
+sa_samples = mcmc_sa.get_samples()
+F_mean_sa = float(sa_samples["F"].mean())
+print(
+    f"\nSA: F = {F_mean_sa:.2f} ± {float(sa_samples['F'].std()):.2f} (true: {TRUE_F}), time: {sa_time:.1f}s"
+)
+# SA is expected to perform poorly — no assertion on accuracy
+
+# ── Cleanup ─────────────────────────────────────────────────────────────
+print("\n=== Cleanup ===")
+lorenz.teardown()
+lorenz_fd.teardown()
+
+print("\n=== ALL STEPS PASSED ===")
diff --git a/demo/bayesian-inference/demo.ipynb b/demo/bayesian-inference/demo.ipynb
new file mode 100644
index 000000000..fa3e4ea48
--- /dev/null
+++ b/demo/bayesian-inference/demo.ipynb
@@ -0,0 +1,758 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simulator-in-the-Loop Bayesian Inference with Tesseract\n",
+    "\n",
+    "In this tutorial, you will learn how to use a Tesseract as the forward model inside a [NumPyro](https://num.pyro.ai/) probabilistic programming workflow. We will:\n",
+    "\n",
+    "1. **Wrap a simulator as a Tesseract** — the Lorenz 96 chaotic dynamical system\n",
+    "2. **Embed it in a NumPyro model** — using [tesseract-jax](https://github.com/pasteurlabs/tesseract-jax) to make the Tesseract a native JAX primitive\n",
+    "3. **Run Bayesian inference** — recover posterior distributions over simulator parameters from noisy observations using NUTS (gradient-based MCMC)\n",
+    "4. **Compare gradient strategies** — analytical (JAX autodiff) vs. finite differences vs. gradient-free sampling\n",
+    "\n",
+    "## Why this matters\n",
+    "\n",
+    "Probabilistic programming languages (PPLs) need a forward model they can differentiate through. Connecting a simulator to a PPL usually means writing a custom wrapper that exposes the simulator and its gradients to the sampler by hand. With Tesseract, you wrap the simulator once and it becomes a JAX-differentiable primitive that plugs directly into NumPyro (or any JAX-based PPL). The same Tesseract is also deployable, composable, and reusable in optimization, ML pipelines, and other contexts.\n",
+    "\n",
+    "## The problem\n",
+    "\n",
+    "We have a chaotic dynamical system (Lorenz 96) with an unknown forcing parameter $F$. We observe a noisy, short trajectory and want to recover the posterior distribution $p(F \\mid \\text{observations})$. This is a classic inverse problem that arises in weather forecasting, climate science, and many other fields.\n",
+    "\n",
+    "Because Lorenz 96 is chaotic, the likelihood landscape is rugged for long integration windows. We work with short observation windows where the model response to $F$ is smooth — exactly the regime where gradient-based sampling shines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -r requirements.txt -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Build and serve the Tesseract\n",
+    "\n",
+    "We use the Lorenz 96 system — a standard benchmark in data assimilation and UQ. The `lorenz_tesseract` folder contains a JAX-based Tesseract with analytical gradients (autodiff via `jax.vjp`) and a differentiable forcing parameter $F$. We also have a `lorenz_tesseract_finitediff` variant that uses pure NumPy and finite-difference gradients, simulating an opaque simulator where source code isn't available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2K \u001b[1;2m[\u001b[0m\u001b[34mi\u001b[0m\u001b[1;2m]\u001b[0m Building image \u001b[33m...\u001b[0m\n",
+      "\u001b[2K\u001b[37m⠧\u001b[0m \u001b[37mProcessing\u001b[0m\n",
+      "\u001b[1A\u001b[2K \u001b[1;2m[\u001b[0m\u001b[34mi\u001b[0m\u001b[1;2m]\u001b[0m Built image sh\u001b[1;92ma256:bd09\u001b[0m673eebaf, \u001b[1m[\u001b[0m\u001b[32m'lorenz-bayesian:0.1.0'\u001b[0m, \u001b[32m'lorenz-bayesian:latest'\u001b[0m\u001b[1m]\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\"lorenz-bayesian:0.1.0\", \"lorenz-bayesian:latest\"]\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "tesseract build lorenz_tesseract/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tesseract_core import Tesseract\n",
+    "\n",
+    "lorenz = Tesseract.from_image(\"lorenz-bayesian\")\n",
+    "lorenz.serve()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Generate synthetic observations\n",
+    "\n",
+    "We generate ground-truth data by running the Lorenz 96 model with the true forcing $F = 18$, then add Gaussian noise to simulate noisy measurements. We use short observation windows (10 time steps apart) to keep the likelihood landscape well-behaved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "System dimension: 8\n",
+      "Number of observations: 3\n",
+      "Observations shape: (3, 8)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from tesseract_jax import apply_tesseract\n",
+    "\n",
+    "# Load an initial condition from the two-scale Lorenz 96 system\n",
+    "data = np.load(\"lorenz96_two_scale_F_18_sample_0_small.npz\")\n",
+    "X_states = data[\"X_states\"]\n",
+    "true_trajectory = X_states[500:]  # skip transients\n",
+    "X0 = true_trajectory[0]\n",
+    "N_DIM = X0.shape[0]  # 8 slow variables\n",
+    "\n",
+    "# Observation setup\n",
+    "OBS_GAP = 10  # time steps between observations\n",
+    "N_OBS = 3  # number of observations\n",
+    "STD_OBS = 0.5  # observation noise standard deviation\n",
+    "TRUE_F = 18.0  # true forcing parameter\n",
+    "N_STEPS = OBS_GAP * N_OBS\n",
+    "\n",
+    "x0_jax = jnp.array(X0, dtype=jnp.float32)\n",
+    "\n",
+    "# Generate ground-truth trajectory with the true F\n",
+    "true_result = apply_tesseract(\n",
+    "    lorenz,\n",
+    "    {\"state\": x0_jax, \"F\": jnp.float32(TRUE_F), \"dt\": 0.005, \"n_steps\": N_STEPS},\n",
+    ")\n",
+    "true_traj = true_result[\"result\"]\n",
+    "\n",
+    "# Extract states at observation times and add noise\n",
+    "obs_indices = jnp.arange(OBS_GAP - 1, N_STEPS, OBS_GAP)\n",
+    "true_obs = true_traj[obs_indices]\n",
+    "\n",
+    "key = jax.random.PRNGKey(42)\n",
+    "observations = true_obs + STD_OBS * jax.random.normal(key, true_obs.shape)\n",
+    "\n",
+    "print(f\"System dimension: {N_DIM}\")\n",
+    "print(f\"Number of observations: {N_OBS}\")\n",
+    "print(f\"Observations shape: {observations.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the ground truth trajectory and observations\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(10, 6), sharex=True)\n",
+    "\n",
+    "for pos, ax in enumerate(axes):\n",
+    "    ax.plot(np.array(true_traj[:, pos]), label=f\"True trajectory $X_{pos}$\", color=\"C1\")\n",
+    "    ax.plot(\n",
+    "        np.array(obs_indices),\n",
+    "        np.array(observations[:, pos]),\n",
+    "        \"ko\",\n",
+    "        ms=6,\n",
+    "        label=\"Observations\",\n",
+    "    )\n",
+    "    ax.set_ylabel(\"Amplitude\")\n",
+    "    ax.legend(loc=\"upper right\")\n",
+    "\n",
+    "axes[-1].set_xlabel(\"Time steps\")\n",
+    "axes[0].set_title(f\"Lorenz 96 (F={TRUE_F}) with noisy observations\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Define the NumPyro model\n",
+    "\n",
+    "This is the key integration. We use `apply_tesseract` from [tesseract-jax](https://github.com/pasteurlabs/tesseract-jax) to call the Tesseract as a JAX primitive inside a NumPyro probabilistic model.\n",
+    "\n",
+    "Because `apply_tesseract` registers the Tesseract as a proper JAX operation (with `abstract_eval` and `vector_jacobian_product`), NumPyro's NUTS sampler can differentiate through it automatically. No custom `jax.pure_callback` or `jax.custom_vjp` wiring needed.\n",
+    "\n",
+    "**The model:**\n",
+    "- **Prior:** $F \\sim \\text{Normal}(15, 5)$ — we have a rough idea the forcing is around 10–20\n",
+    "- **Forward model:** Integrate Lorenz 96 from the known initial condition $X_0$ with forcing $F$\n",
+    "- **Likelihood:** $y_i \\sim \\text{Normal}(x_{t_i}, \\sigma_{\\text{obs}})$ — observations are noisy measurements of the state at discrete times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpyro\n",
+    "import numpyro.distributions as dist\n",
+    "\n",
+    "\n",
+    "def bayesian_lorenz_model(observations, x0, obs_gap, n_obs, std_obs):\n",
+    "    \"\"\"NumPyro model for Bayesian inference over Lorenz 96 forcing parameter.\"\"\"\n",
+    "    # Prior on the forcing parameter F\n",
+    "    F = numpyro.sample(\"F\", dist.Normal(15.0, 5.0))\n",
+    "\n",
+    "    # Forward model: integrate Lorenz 96 using the Tesseract\n",
+    "    result = apply_tesseract(\n",
+    "        lorenz,\n",
+    "        {\n",
+    "            \"state\": x0,\n",
+    "            \"F\": F,\n",
+    "            \"dt\": 0.005,\n",
+    "            \"n_steps\": obs_gap * n_obs,\n",
+    "        },\n",
+    "    )\n",
+    "    trajectory = result[\"result\"]  # shape: (obs_gap * n_obs, N_DIM)\n",
+    "\n",
+    "    # Extract predicted states at observation times\n",
+    "    obs_indices = jnp.arange(obs_gap - 1, obs_gap * n_obs, obs_gap)\n",
+    "    predicted_obs = trajectory[obs_indices]  # shape: (n_obs, N_DIM)\n",
+    "\n",
+    "    # Likelihood: observations are noisy measurements of predicted states\n",
+    "    numpyro.sample(\n",
+    "        \"obs\",\n",
+    "        dist.Normal(predicted_obs, std_obs),\n",
+    "        obs=observations,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Run NUTS inference\n",
+    "\n",
+    "NUTS (No-U-Turn Sampler) is a gradient-based MCMC algorithm that uses the gradient of the log-posterior to propose moves. Because `apply_tesseract` is a proper JAX primitive, `jax.grad` flows through the Tesseract automatically — NUTS just works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sample: 100%|██████████| 700/700 [00:46<00:00, 15.16it/s, 1 steps of size 9.66e-01. acc. prob=0.94]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "         F     18.00      0.96     18.01     16.57     19.49    220.04      1.00\n",
+      "\n",
+      "Number of divergences: 0\n",
+      "\n",
+      "NUTS wall time: 48.1s\n",
+      "True F = 18.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "from numpyro.infer import MCMC, NUTS\n",
+    "\n",
+    "# Run NUTS\n",
+    "nuts_kernel = NUTS(bayesian_lorenz_model)\n",
+    "mcmc_nuts = MCMC(nuts_kernel, num_warmup=200, num_samples=500, num_chains=1)\n",
+    "\n",
+    "start = time.time()\n",
+    "mcmc_nuts.run(\n",
+    "    jax.random.PRNGKey(0),\n",
+    "    observations=observations,\n",
+    "    x0=x0_jax,\n",
+    "    obs_gap=OBS_GAP,\n",
+    "    n_obs=N_OBS,\n",
+    "    std_obs=STD_OBS,\n",
+    ")\n",
+    "nuts_time = time.time() - start\n",
+    "\n",
+    "mcmc_nuts.print_summary()\n",
+    "print(f\"\\nNUTS wall time: {nuts_time:.1f}s\")\n",
+    "print(f\"True F = {TRUE_F}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Visualize the posterior\n",
+    "\n",
+    "Let's inspect the posterior distribution over $F$. The posterior should be concentrated around the true value $F = 18$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x350 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Posterior mean: F = 18.00 ± 0.96\n",
+      "True value:     F = 18.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import arviz as az\n",
+    "\n",
+    "nuts_samples = mcmc_nuts.get_samples()\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(10, 3.5))\n",
+    "\n",
+    "# Trace plot\n",
+    "axes[0].plot(np.array(nuts_samples[\"F\"]), color=\".2\", lw=0.5)\n",
+    "axes[0].axhline(TRUE_F, color=\"C1\", ls=\"--\", label=f\"True F = {TRUE_F}\")\n",
+    "axes[0].set_xlabel(\"Sample\")\n",
+    "axes[0].set_ylabel(\"F\")\n",
+    "axes[0].set_title(\"NUTS trace\")\n",
+    "axes[0].legend()\n",
+    "\n",
+    "# Posterior density\n",
+    "axes[1].hist(np.array(nuts_samples[\"F\"]), bins=30, density=True, color=\".3\", alpha=0.7)\n",
+    "axes[1].axvline(TRUE_F, color=\"C1\", ls=\"--\", lw=2, label=f\"True F = {TRUE_F}\")\n",
+    "axes[1].set_xlabel(\"F\")\n",
+    "axes[1].set_ylabel(\"Density\")\n",
+    "axes[1].set_title(\"Posterior p(F | observations)\")\n",
+    "axes[1].legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "F_mean = float(nuts_samples[\"F\"].mean())\n",
+    "F_std = float(nuts_samples[\"F\"].std())\n",
+    "print(f\"Posterior mean: F = {F_mean:.2f} ± {F_std:.2f}\")\n",
+    "print(f\"True value:     F = {TRUE_F}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: The opaque simulator scenario\n",
+    "\n",
+    "What if your simulator isn't written in JAX? What if it's a compiled Fortran binary, a commercial solver, or legacy code you can't modify?\n",
+    "\n",
+    "Tesseract handles this too. The `lorenz_tesseract_finitediff` variant implements the same Lorenz 96 physics in **pure NumPy** (no JAX at all) and uses **finite-difference gradients**. From the outside, it exposes the same API — same `apply`, same `vector_jacobian_product` — so the NumPyro model doesn't change at all.\n",
+    "\n",
+    "Let's build and run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2K \u001b[1;2m[\u001b[0m\u001b[34mi\u001b[0m\u001b[1;2m]\u001b[0m Building image \u001b[33m...\u001b[0m\n",
+      "\u001b[2K\u001b[37m⠇\u001b[0m \u001b[37mProcessing\u001b[0m\n",
+      "\u001b[1A\u001b[2K \u001b[1;2m[\u001b[0m\u001b[34mi\u001b[0m\u001b[1;2m]\u001b[0m Built image sh\u001b[1;92ma256:3308\u001b[0m3963ab1c, \u001b[1m[\u001b[0m\u001b[32m'lorenz-finitediff:0.1.0'\u001b[0m, \u001b[32m'lorenz-finitediff:latest'\u001b[0m\u001b[1m]\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\"lorenz-finitediff:0.1.0\", \"lorenz-finitediff:latest\"]\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "tesseract build lorenz_tesseract_finitediff/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lorenz_fd = Tesseract.from_image(\"lorenz-finitediff\")\n",
+    "lorenz_fd.serve()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sample: 100%|██████████| 700/700 [00:56<00:00, 12.44it/s, 3 steps of size 8.48e-01. acc. prob=0.92]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "         F     18.06      1.00     18.02     16.48     19.55    178.70      1.00\n",
+      "\n",
+      "Number of divergences: 0\n",
+      "\n",
+      "NUTS (finite-diff) wall time: 56.6s\n",
+      "True F = 18.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def bayesian_lorenz_model_fd(observations, x0, obs_gap, n_obs, std_obs):\n",
+    "    \"\"\"Same model, but using the finite-difference Tesseract.\"\"\"\n",
+    "    F = numpyro.sample(\"F\", dist.Normal(15.0, 5.0))\n",
+    "\n",
+    "    result = apply_tesseract(\n",
+    "        lorenz_fd,\n",
+    "        {\n",
+    "            \"state\": x0,\n",
+    "            \"F\": F,\n",
+    "            \"dt\": 0.005,\n",
+    "            \"n_steps\": obs_gap * n_obs,\n",
+    "        },\n",
+    "    )\n",
+    "    trajectory = result[\"result\"]\n",
+    "    obs_indices = jnp.arange(obs_gap - 1, obs_gap * n_obs, obs_gap)\n",
+    "    predicted_obs = trajectory[obs_indices]\n",
+    "\n",
+    "    numpyro.sample(\"obs\", dist.Normal(predicted_obs, std_obs), obs=observations)\n",
+    "\n",
+    "\n",
+    "# Run NUTS with finite-diff gradients\n",
+    "nuts_fd_kernel = NUTS(bayesian_lorenz_model_fd)\n",
+    "mcmc_nuts_fd = MCMC(nuts_fd_kernel, num_warmup=200, num_samples=500, num_chains=1)\n",
+    "\n",
+    "start = time.time()\n",
+    "mcmc_nuts_fd.run(\n",
+    "    jax.random.PRNGKey(0),\n",
+    "    observations=observations,\n",
+    "    x0=x0_jax,\n",
+    "    obs_gap=OBS_GAP,\n",
+    "    n_obs=N_OBS,\n",
+    "    std_obs=STD_OBS,\n",
+    ")\n",
+    "nuts_fd_time = time.time() - start\n",
+    "\n",
+    "mcmc_nuts_fd.print_summary()\n",
+    "print(f\"\\nNUTS (finite-diff) wall time: {nuts_fd_time:.1f}s\")\n",
+    "print(f\"True F = {TRUE_F}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Gradient-free baseline\n",
+    "\n",
+    "For comparison, let's run a gradient-free sampler. This mirrors the workflow without Tesseract's gradient endpoints — treating the simulator as a black-box function with no derivative information.\n",
+    "\n",
+    "We use NumPyro's Stochastic Approximation (SA) sampler, which doesn't require gradients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sample: 100%|██████████| 1500/1500 [00:09<00:00, 157.83it/s, acc. prob=0.39]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "         F      5.91      0.92      6.13      4.21      7.17      3.55      1.85\n",
+      "\n",
+      "Number of divergences: 0\n",
+      "\n",
+      "SA (gradient-free) wall time: 10.4s\n",
+      "True F = 18.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from numpyro.infer import SA\n",
+    "\n",
+    "# SA is gradient-free — it doesn't need VJP endpoints\n",
+    "sa_kernel = SA(bayesian_lorenz_model)\n",
+    "mcmc_sa = MCMC(sa_kernel, num_warmup=1000, num_samples=500, num_chains=1)\n",
+    "\n",
+    "start = time.time()\n",
+    "mcmc_sa.run(\n",
+    "    jax.random.PRNGKey(0),\n",
+    "    observations=observations,\n",
+    "    x0=x0_jax,\n",
+    "    obs_gap=OBS_GAP,\n",
+    "    n_obs=N_OBS,\n",
+    "    std_obs=STD_OBS,\n",
+    ")\n",
+    "sa_time = time.time() - start\n",
+    "\n",
+    "mcmc_sa.print_summary()\n",
+    "print(f\"\\nSA (gradient-free) wall time: {sa_time:.1f}s\")\n",
+    "print(f\"True F = {TRUE_F}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 8: Comparison\n",
+    "\n",
+    "Let's compare all three approaches side by side: posterior quality, mixing, and efficiency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nuts_fd_samples = mcmc_nuts_fd.get_samples()\n",
+    "sa_samples = mcmc_sa.get_samples()\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 3, figsize=(14, 6))\n",
+    "\n",
+    "configs = [\n",
+    "    (\"NUTS (JAX autodiff)\", nuts_samples, nuts_time, \"C0\"),\n",
+    "    (\"NUTS (finite-diff)\", nuts_fd_samples, nuts_fd_time, \"C2\"),\n",
+    "    (\"SA (gradient-free)\", sa_samples, sa_time, \"C3\"),\n",
+    "]\n",
+    "\n",
+    "for i, (label, samples, wall_time, color) in enumerate(configs):\n",
+    "    F_vals = np.array(samples[\"F\"])\n",
+    "\n",
+    "    # Trace plot\n",
+    "    axes[0, i].plot(F_vals, color=color, lw=0.5, alpha=0.8)\n",
+    "    axes[0, i].axhline(TRUE_F, color=\"k\", ls=\"--\", lw=1)\n",
+    "    axes[0, i].set_title(f\"{label}\\n({wall_time:.1f}s)\")\n",
+    "    axes[0, i].set_ylabel(\"F\")\n",
+    "    axes[0, i].set_xlabel(\"Sample\")\n",
+    "\n",
+    "    # Posterior histogram\n",
+    "    axes[1, i].hist(F_vals, bins=30, density=True, color=color, alpha=0.7)\n",
+    "    axes[1, i].axvline(TRUE_F, color=\"k\", ls=\"--\", lw=2, label=\"True F\")\n",
+    "    axes[1, i].set_xlabel(\"F\")\n",
+    "    axes[1, i].set_ylabel(\"Density\")\n",
+    "    axes[1, i].legend()\n",
+    "\n",
+    "plt.suptitle(\"Comparison: gradient-based vs gradient-free sampling\", fontsize=13)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Method       | Posterior mean | Posterior std | ESS | Wall time (s) | ESS/sec \n",
+      "---------------------|----------------|---------------|-----|---------------|---------\n",
+      " NUTS (JAX autodiff) |     18.00      |      0.96     | 224 |      48.1     |   4.7   \n",
+      "  NUTS (finite-diff) |     18.06      |      1.00     | 190 |      56.6     |   3.4   \n",
+      "  SA (gradient-free) |      5.91      |      0.92     |  1  |      10.4     |   0.1   \n",
+      "\n",
+      "True F = 18.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Quantitative comparison\n",
+    "def compute_ess(samples):\n",
+    "    \"\"\"Compute effective sample size using arviz.\"\"\"\n",
+    "    return float(az.ess(samples))\n",
+    "\n",
+    "\n",
+    "results = []\n",
+    "for label, samples, wall_time, _ in configs:\n",
+    "    F_vals = np.array(samples[\"F\"])\n",
+    "    ess = compute_ess(F_vals)\n",
+    "    results.append(\n",
+    "        {\n",
+    "            \"Method\": label,\n",
+    "            \"Posterior mean\": f\"{F_vals.mean():.2f}\",\n",
+    "            \"Posterior std\": f\"{F_vals.std():.2f}\",\n",
+    "            \"ESS\": f\"{ess:.0f}\",\n",
+    "            \"Wall time (s)\": f\"{wall_time:.1f}\",\n",
+    "            \"ESS/sec\": f\"{ess / wall_time:.1f}\",\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "# Print as a table\n",
+    "header = results[0].keys()\n",
+    "col_widths = {k: max(len(k), max(len(r[k]) for r in results)) + 2 for k in header}\n",
+    "header_str = \"|\".join(k.center(col_widths[k]) for k in header)\n",
+    "sep_str = \"|\".join(\"-\" * col_widths[k] for k in header)\n",
+    "print(header_str)\n",
+    "print(sep_str)\n",
+    "for r in results:\n",
+    "    print(\"|\".join(r[k].center(col_widths[k]) for k in header))\n",
+    "\n",
+    "print(f\"\\nTrue F = {TRUE_F}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lorenz.teardown()\n",
+    "lorenz_fd.teardown()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Takeaways\n",
+    "\n",
+    "1. **Tesseract + tesseract-jax makes simulators first-class citizens in NumPyro.** No custom callbacks, no manual VJP wiring — just `apply_tesseract` inside your model.\n",
+    "\n",
+    "2. **Gradients dramatically improve sampling efficiency.** NUTS with gradients (whether analytical or finite-difference) recovers the true parameter and achieves high ESS, while the gradient-free sampler fails to converge entirely.\n",
+    "\n",
+    "3. **Opaque simulators work too.** Even without JAX autodiff, finite-difference gradients via Tesseract's experimental API give NUTS enough information to sample efficiently.\n",
+    "\n",
+    "4. **One Tesseract, many contexts.** The same Lorenz 96 Tesseract we used here also works in the [4D-Var data assimilation demo](data-assimilation.ipynb), in optimization pipelines, and as a deployed service. Wrap once, use everywhere."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What's next\n",
+    "\n",
+    "- **Try a different prior or observation setup.** Adjust `OBS_GAP`, `N_OBS`, or the prior on $F$ to see how data quantity and prior strength shape the posterior.\n",
+    "- **Infer more parameters.** Extend the NumPyro model to recover the initial condition $X_0$ alongside the forcing $F$.\n",
+    "- **Swap the sampler.** Try other gradient-based kernels (e.g. HMC) or run multiple chains to assess convergence.\n",
+    "- **Explore other demos.** The same Lorenz 96 Tesseract also powers the [4D-Var data assimilation demo](data-assimilation.ipynb) — another way to compose Tesseracts with JAX.\n",
+    "\n",
+    "Questions? Feedback? Please reach out through the [Tesseract Community Forum](https://si-tesseract.discourse.group/)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "defaultInterpreterPath: 3.12.7.final.0",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/demo/bayesian-inference/lorenz96_two_scale_F_18_sample_0_small.npz b/demo/bayesian-inference/lorenz96_two_scale_F_18_sample_0_small.npz
new file mode 100644
index 000000000..240ad8940
Binary files /dev/null and b/demo/bayesian-inference/lorenz96_two_scale_F_18_sample_0_small.npz differ
diff --git a/demo/bayesian-inference/lorenz_tesseract/tesseract_api.py b/demo/bayesian-inference/lorenz_tesseract/tesseract_api.py
new file mode 100644
index 000000000..f5305ecc0
--- /dev/null
+++ b/demo/bayesian-inference/lorenz_tesseract/tesseract_api.py
@@ -0,0 +1,186 @@
+# Copyright 2025 Pasteur Labs. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Lorenz 96 Tesseract with differentiable forcing parameter F.
+
+This is a variant of the Lorenz 96 Tesseract from the 4D-Var demo, modified
+to mark the forcing parameter F as Differentiable. This allows JAX autodiff
+(and thus NumPyro's NUTS) to compute gradients w.r.t. F for Bayesian
+parameter inference.
+"""
+
+from typing import Any
+
+import equinox as eqx
+import jax
+import jax.numpy as jnp
+from pydantic import BaseModel, Field
+
+from tesseract_core.runtime import Array, Differentiable, Float32
+from tesseract_core.runtime.tree_transforms import filter_func, flatten_with_paths
+
+
+class InputSchema(BaseModel):
+    """Input schema for forecasting of Lorenz 96 system."""
+
+    state: Differentiable[Array[(None,), Float32]] = Field(
+        description="A state vector for the Lorenz 96 system"
+    )
+    F: Differentiable[Array[(), Float32]] = Field(
+        description="Forcing parameter for Lorenz 96", default=8.0
+    )
+    dt: float = Field(description="Time step for integration", default=0.05)
+    n_steps: int = Field(description="Number of integration steps", default=1)
+
+
+class OutputSchema(BaseModel):
+    """Output schema for forecasting of Lorenz 96 system."""
+
+    result: Differentiable[Array[(None, None), Float32]] = Field(
+        description="A trajectory of predictions after integration"
+    )
+
+
+def lorenz96_step(state: jnp.ndarray, F: float, dt: float) -> jnp.ndarray:
+    """Perform one step of RK4 integration for the Lorenz 96 system."""
+
+    def lorenz96_derivatives(x: jnp.ndarray) -> jnp.ndarray:
+        N = x.shape[0]
+        ip1 = (jnp.arange(N) + 1) % N
+        im1 = (jnp.arange(N) - 1) % N
+        im2 = (jnp.arange(N) - 2) % N
+        d = (x[ip1] - x[im2]) * x[im1] - x + F
+        return d
+
+    k1 = lorenz96_derivatives(state)
+    k2 = lorenz96_derivatives(state + dt * k1 / 2)
+    k3 = lorenz96_derivatives(state + dt * k2 / 2)
+    k4 = lorenz96_derivatives(state + dt * k3)
+    return state + dt * (k1 + 2 * k2 + 2 * k3 + k4) / 6
+
+
+def lorenz96_multi_step(
+    state: jnp.ndarray, F: float, dt: float, n_steps: int
+) -> jnp.ndarray:
+    """Perform multiple steps of Lorenz 96 integration using scan."""
+
+    def step_fn(state: jnp.ndarray, _: Any) -> tuple:
+        return lorenz96_step(state, F, dt), state
+
+    _, trajectory = jax.lax.scan(step_fn, state, None, length=n_steps)
+    return trajectory
+
+
+@eqx.filter_jit
+def apply_jit(inputs: dict) -> dict:
+    trajectory = lorenz96_multi_step(**inputs)
+    return dict(result=trajectory)
+
+
+def apply(inputs: InputSchema) -> OutputSchema:
+    return apply_jit(inputs.model_dump())
+
+
+def abstract_eval(abstract_inputs: Any) -> Any:
+    """Calculate output shape of apply from the shape of its inputs."""
+    is_shapedtype_dict = lambda x: type(x) is dict and (x.keys() == {"shape", "dtype"})
+    is_shapedtype_struct = lambda x: isinstance(x, jax.ShapeDtypeStruct)
+
+    jaxified_inputs = jax.tree.map(
+        lambda x: jax.ShapeDtypeStruct(**x) if is_shapedtype_dict(x) else x,
+        abstract_inputs.model_dump(),
+        is_leaf=is_shapedtype_dict,
+    )
+    dynamic_inputs, static_inputs = eqx.partition(
+        jaxified_inputs, filter_spec=is_shapedtype_struct
+    )
+
+    def wrapped_apply(dynamic_inputs: Any) -> Any:
+        inputs = eqx.combine(static_inputs, dynamic_inputs)
+        return apply_jit(inputs)
+
+    jax_shapes = jax.eval_shape(wrapped_apply, dynamic_inputs)
+    return jax.tree.map(
+        lambda x: (
+            {"shape": x.shape, "dtype": str(x.dtype)} if is_shapedtype_struct(x) else x
+        ),
+        jax_shapes,
+        is_leaf=is_shapedtype_struct,
+    )
+
+
+@eqx.filter_jit
+def jac_jit(
+    inputs: dict,
+    jac_inputs: tuple[str],
+    jac_outputs: tuple[str],
+) -> dict:
+    filtered_apply = filter_func(apply_jit, inputs, jac_outputs)
+    return jax.jacrev(filtered_apply)(
+        flatten_with_paths(inputs, include_paths=jac_inputs)
+    )
+
+
+def jacobian(
+    inputs: InputSchema,
+    jac_inputs: set[str],
+    jac_outputs: set[str],
+) -> Any:
+    return jac_jit(inputs.model_dump(), tuple(jac_inputs), tuple(jac_outputs))
+
+
+@eqx.filter_jit
+def jvp_jit(
+    inputs: dict,
+    jvp_inputs: tuple[str],
+    jvp_outputs: tuple[str],
+    tangent_vector: dict,
+) -> Any:
+    filtered_apply = filter_func(apply_jit, inputs, jvp_outputs)
+    return jax.jvp(
+        filtered_apply,
+        [flatten_with_paths(inputs, include_paths=jvp_inputs)],
+        [tangent_vector],
+    )[1]
+
+
+def jacobian_vector_product(
+    inputs: InputSchema,
+    jvp_inputs: set[str],
+    jvp_outputs: set[str],
+    tangent_vector: dict[str, Any],
+) -> Any:
+    return jvp_jit(
+        inputs.model_dump(),
+        tuple(jvp_inputs),
+        tuple(jvp_outputs),
+        tangent_vector,
+    )
+
+
+@eqx.filter_jit
+def vjp_jit(
+    inputs: dict,
+    vjp_inputs: tuple[str],
+    vjp_outputs: tuple[str],
+    cotangent_vector: dict,
+) -> Any:
+    filtered_apply = filter_func(apply_jit, inputs, vjp_outputs)
+    _, vjp_func = jax.vjp(
+        filtered_apply, flatten_with_paths(inputs, include_paths=vjp_inputs)
+    )
+    return vjp_func(cotangent_vector)[0]
+
+
+def vector_jacobian_product(
+    inputs: InputSchema,
+    vjp_inputs: set[str],
+    vjp_outputs: set[str],
+    cotangent_vector: dict[str, Any],
+) -> Any:
+    return vjp_jit(
+        inputs.model_dump(),
+        tuple(vjp_inputs),
+        tuple(vjp_outputs),
+        cotangent_vector,
+    )
diff --git a/demo/bayesian-inference/lorenz_tesseract/tesseract_config.yaml b/demo/bayesian-inference/lorenz_tesseract/tesseract_config.yaml
new file mode 100644
index 000000000..c59c0fa43
--- /dev/null
+++ b/demo/bayesian-inference/lorenz_tesseract/tesseract_config.yaml
@@ -0,0 +1,8 @@
+# Tesseract configuration file
+
+name: "lorenz-bayesian"
+version: "0.1.0"
+description: "Lorenz 96 Tesseract with differentiable forcing parameter for Bayesian inference"
+
+build_config:
+  target_platform: "native"
diff --git a/demo/bayesian-inference/lorenz_tesseract/tesseract_requirements.txt b/demo/bayesian-inference/lorenz_tesseract/tesseract_requirements.txt
new file mode 100644
index 000000000..acf1fe672
--- /dev/null
+++ b/demo/bayesian-inference/lorenz_tesseract/tesseract_requirements.txt
@@ -0,0 +1,4 @@
+# Tesseract requirements file
+numpy==1.26.0
+jax[cpu]==0.5.2
+equinox
diff --git a/demo/bayesian-inference/lorenz_tesseract_finitediff/tesseract_api.py b/demo/bayesian-inference/lorenz_tesseract_finitediff/tesseract_api.py
new file mode 100644
index 000000000..a3846d7d4
--- /dev/null
+++ b/demo/bayesian-inference/lorenz_tesseract_finitediff/tesseract_api.py
@@ -0,0 +1,148 @@
+# Copyright 2025 Pasteur Labs. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Lorenz 96 Tesseract with finite-difference gradients.
+
+This variant of the Lorenz 96 Tesseract uses numerical finite differences
+for all gradient endpoints, simulating the scenario where the simulator
+source code is unavailable or not differentiable (e.g., a compiled Fortran
+binary, a commercial solver, etc.).
+
+The apply function is identical to the JAX variant — only the gradient
+strategy differs.
+"""
+
+from typing import Any
+
+import numpy as np
+from pydantic import BaseModel, Field
+
+from tesseract_core.runtime import Array, Differentiable, Float32, ShapeDType
+from tesseract_core.runtime.experimental import (
+    finite_difference_jacobian,
+    finite_difference_jvp,
+    finite_difference_vjp,
+)
+
+
+class InputSchema(BaseModel):
+    """Input schema for forecasting of Lorenz 96 system."""
+
+    state: Differentiable[Array[(None,), Float32]] = Field(
+        description="A state vector for the Lorenz 96 system"
+    )
+    F: Differentiable[Array[(), Float32]] = Field(
+        description="Forcing parameter for Lorenz 96", default=8.0
+    )
+    dt: float = Field(description="Time step for integration", default=0.05)
+    n_steps: int = Field(description="Number of integration steps", default=1)
+
+
+class OutputSchema(BaseModel):
+    """Output schema for forecasting of Lorenz 96 system."""
+
+    result: Differentiable[Array[(None, None), Float32]] = Field(
+        description="A trajectory of predictions after integration"
+    )
+
+
+# --- Pure NumPy Lorenz 96 solver (no JAX dependency) ---
+
+
+def _lorenz96_derivatives(x: np.ndarray, F: float) -> np.ndarray:
+    """Compute Lorenz 96 tendencies."""
+    N = x.shape[0]
+    ip1 = (np.arange(N) + 1) % N
+    im1 = (np.arange(N) - 1) % N
+    im2 = (np.arange(N) - 2) % N
+    return (x[ip1] - x[im2]) * x[im1] - x + F
+
+
+def _lorenz96_step(state: np.ndarray, F: float, dt: float) -> np.ndarray:
+    """One RK4 step."""
+    k1 = _lorenz96_derivatives(state, F)
+    k2 = _lorenz96_derivatives(state + dt * k1 / 2, F)
+    k3 = _lorenz96_derivatives(state + dt * k2 / 2, F)
+    k4 = _lorenz96_derivatives(state + dt * k3, F)
+    return state + dt * (k1 + 2 * k2 + 2 * k3 + k4) / 6
+
+
+def _lorenz96_multi_step(
+    state: np.ndarray, F: float, dt: float, n_steps: int
+) -> np.ndarray:
+    """Integrate Lorenz 96 for n_steps, returning the full trajectory."""
+    trajectory = np.empty((n_steps, state.shape[0]), dtype=np.float32)
+    for i in range(n_steps):
+        next_state = _lorenz96_step(state, F, dt)
+        trajectory[i] = state
+        state = next_state
+    return trajectory
+
+
+# --- Tesseract endpoints ---
+
+
+def apply(inputs: InputSchema) -> OutputSchema:
+    """Forward pass: integrate the Lorenz 96 system."""
+    state = np.asarray(inputs.state, dtype=np.float64)
+    F = float(inputs.F)
+    trajectory = _lorenz96_multi_step(state, F, inputs.dt, inputs.n_steps)
+    return OutputSchema(result=trajectory.astype(np.float32))
+
+
+def abstract_eval(abstract_inputs: Any) -> Any:
+    """Calculate output shape from input shape."""
+    state_sd = abstract_inputs.state
+    n_dim = state_sd.shape[0]
+    # n_steps is a static int, available as a concrete value
+    n_steps = abstract_inputs.n_steps
+    return {
+        "result": ShapeDType(shape=(n_steps, n_dim), dtype="float32"),
+    }
+
+
+def jacobian(
+    inputs: InputSchema,
+    jac_inputs: set[str],
+    jac_outputs: set[str],
+) -> Any:
+    """Jacobian via central finite differences."""
+    return finite_difference_jacobian(
+        apply, inputs, jac_inputs, jac_outputs, algorithm="central", eps=1e-5
+    )
+
+
+def jacobian_vector_product(
+    inputs: InputSchema,
+    jvp_inputs: set[str],
+    jvp_outputs: set[str],
+    tangent_vector: dict[str, Any],
+) -> Any:
+    """JVP via finite differences."""
+    return finite_difference_jvp(
+        apply,
+        inputs,
+        jvp_inputs,
+        jvp_outputs,
+        tangent_vector,
+        algorithm="central",
+        eps=1e-5,
+    )
+
+
+def vector_jacobian_product(
+    inputs: InputSchema,
+    vjp_inputs: set[str],
+    vjp_outputs: set[str],
+    cotangent_vector: dict[str, Any],
+) -> Any:
+    """VJP via finite differences."""
+    return finite_difference_vjp(
+        apply,
+        inputs,
+        vjp_inputs,
+        vjp_outputs,
+        cotangent_vector,
+        algorithm="central",
+        eps=1e-5,
+    )
diff --git a/demo/bayesian-inference/lorenz_tesseract_finitediff/tesseract_config.yaml b/demo/bayesian-inference/lorenz_tesseract_finitediff/tesseract_config.yaml
new file mode 100644
index 000000000..3dbd9a44a
--- /dev/null
+++ b/demo/bayesian-inference/lorenz_tesseract_finitediff/tesseract_config.yaml
@@ -0,0 +1,8 @@
+# Tesseract configuration file
+
+name: "lorenz-finitediff"
+version: "0.1.0"
+description: "Lorenz 96 Tesseract with finite-difference gradients (opaque simulator scenario)"
+
+build_config:
+  target_platform: "native"
diff --git a/demo/bayesian-inference/lorenz_tesseract_finitediff/tesseract_requirements.txt b/demo/bayesian-inference/lorenz_tesseract_finitediff/tesseract_requirements.txt
new file mode 100644
index 000000000..4f2ec81f2
--- /dev/null
+++ b/demo/bayesian-inference/lorenz_tesseract_finitediff/tesseract_requirements.txt
@@ -0,0 +1,2 @@
+# No JAX needed — this simulates an opaque simulator
+numpy==1.26.0
diff --git a/demo/bayesian-inference/requirements.txt b/demo/bayesian-inference/requirements.txt
new file mode 100644
index 000000000..8dffa5340
--- /dev/null
+++ b/demo/bayesian-inference/requirements.txt
@@ -0,0 +1,12 @@
+# Pinned to a verified-working set that also supports Python 3.10 (the project's
+# floor). The notebook runs end-to-end on 3.10 with these. Two constraints drive
+# the pins:
+#   - Python 3.10: jax > 0.6.2 and numpy >= 2.3 require >= 3.11, which in turn
+#     caps numpyro at 0.19 (later releases need jax >= 0.7 / Python >= 3.11).
+#   - arviz >= 1.0 dropped support for az.ess() on a bare ndarray.
+jax[cpu]==0.6.2
+numpy==2.2.6
+numpyro==0.19.0
+arviz==0.23.4
+matplotlib==3.10.9
+tesseract-jax==0.3.0
diff --git a/docs/content/demo/demo.md b/docs/content/demo/demo.md
index 8b05b53c6..3eee983bc 100644
--- a/docs/content/demo/demo.md
+++ b/docs/content/demo/demo.md
@@ -7,6 +7,7 @@ End-to-end examples that show Tesseracts in action — from optimization workflo
 :hidden:
 
 data-assimilation.ipynb
+bayesian-inference.ipynb
 lorenz_tesseract.md
 cfd-optimization.ipynb
 fem-shape-optimization.ipynb
@@ -38,6 +39,11 @@ Full walkthrough of a 4D-Var scheme using a differentiable Lorenz-96 Tesseract 
 
 Detailed implementation of the JAX-based Lorenz-96 solver Tesseract used in the 4D-Var demo.
 :::
+:::{grid-item-card} Bayesian Inference
+:link: bayesian-inference.html
+
+Use the same Lorenz-96 Tesseract as the forward model inside a NumPyro probabilistic workflow — recover the posterior over an unknown forcing parameter with gradient-based MCMC.
+:::
 
 ::::
 
diff --git a/docs/index.md b/docs/index.md
index c5dc17053..f10a53f34 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -227,7 +227,7 @@ Ready to build your own? The {doc}`Get Started <content/introduction/get-started
 
 ## Demos
 
-::::{grid} 1 2 3 3
+::::{grid} 1 2 2 4
 :gutter: 3
 
 :::{grid-item-card} 4D-Var Data Assimilation
@@ -241,6 +241,17 @@ A complete 4D-Variational data assimilation scheme for a chaotic dynamical
 system (Lorenz-96), built with a differentiable JAX Tesseract.
 :::
 
+:::{grid-item-card} Bayesian Inference
+:link: content/demo/bayesian-inference
+:link-type: doc
+:class-card: demo-card
+:img-top: static/demo-bayesian-inference.svg
+:class-img-top: demo-schematic invert-on-dark
+
+Use a Lorenz-96 Tesseract as the forward model in a NumPyro workflow and
+recover the posterior over an unknown forcing parameter with gradient-based MCMC.
+:::
+
 :::{grid-item-card} CFD Flow Optimization
 :link: content/demo/cfd-optimization
 :link-type: doc
diff --git a/docs/static/demo-bayesian-inference.svg b/docs/static/demo-bayesian-inference.svg
new file mode 100644
index 000000000..8588d98ee
--- /dev/null
+++ b/docs/static/demo-bayesian-inference.svg
@@ -0,0 +1,47 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 280 120" fill="none">
+  <!-- Bayesian Inference: prior p(F) → Lorenz-96 Tesseract → posterior p(F|obs), driven by ∇-based NUTS sampling -->
+
+  <!-- Prior -->
+  <rect x="4" y="35" width="52" height="50" rx="6" stroke="#222" stroke-width="1.2"/>
+  <!-- Prior axis + broad bump -->
+  <line x1="12" y1="74" x2="48" y2="74" stroke="#555" stroke-width="0.6"/>
+  <path d="M 12,74 C 22,74 24,52 30,52 C 36,52 38,74 48,74" stroke="#222" stroke-width="1" fill="none"/>
+  <text x="30" y="30" text-anchor="middle" font-family="system-ui, sans-serif" font-size="7.5" fill="#555">prior p(F)</text>
+
+  <!-- Arrow 1 -->
+  <line x1="56" y1="60" x2="78" y2="60" stroke="#222" stroke-width="1"/>
+  <polygon points="78,57 84,60 78,63" fill="#222"/>
+
+  <!-- Lorenz-96 Tesseract (forward model) -->
+  <rect x="86" y="35" width="62" height="50" rx="6" stroke="#222" stroke-width="1.2"/>
+  <!-- Inner tesseract symbol -->
+  <rect x="104" y="46" width="14" height="14" rx="1" stroke="#222" stroke-width="0.8" stroke-dasharray="2 1.5"/>
+  <rect x="108" y="50" width="6" height="6" rx="0.5" stroke="#222" stroke-width="0.8"/>
+  <text x="117" y="77" text-anchor="middle" font-family="system-ui, sans-serif" font-size="7" fill="#555">Lorenz-96</text>
+
+  <!-- Arrow 2 -->
+  <line x1="148" y1="60" x2="170" y2="60" stroke="#222" stroke-width="1"/>
+  <polygon points="170,57 176,60 170,63" fill="#222"/>
+
+  <!-- Posterior with true value -->
+  <rect x="178" y="35" width="98" height="50" rx="6" stroke="#222" stroke-width="1.2"/>
+  <!-- Posterior axis -->
+  <line x1="188" y1="74" x2="266" y2="74" stroke="#555" stroke-width="0.6"/>
+  <!-- Histogram-like samples under the peak -->
+  <line x1="214" y1="74" x2="214" y2="66" stroke="#555" stroke-width="0.6"/>
+  <line x1="220" y1="74" x2="220" y2="58" stroke="#555" stroke-width="0.6"/>
+  <line x1="226" y1="74" x2="226" y2="50" stroke="#555" stroke-width="0.6"/>
+  <line x1="232" y1="74" x2="232" y2="58" stroke="#555" stroke-width="0.6"/>
+  <line x1="238" y1="74" x2="238" y2="66" stroke="#555" stroke-width="0.6"/>
+  <!-- Sharp posterior bump -->
+  <path d="M 196,74 C 216,74 218,46 226,46 C 234,46 236,74 256,74" stroke="#222" stroke-width="1" fill="none"/>
+  <!-- True-value tick -->
+  <line x1="226" y1="44" x2="226" y2="74" stroke="#222" stroke-width="0.8" stroke-dasharray="2 2"/>
+  <text x="226" y="40" text-anchor="middle" font-family="system-ui, sans-serif" font-size="6" fill="#555">true F</text>
+  <text x="227" y="82" text-anchor="middle" font-family="system-ui, sans-serif" font-size="7" fill="#555">posterior p(F | obs)</text>
+
+  <!-- Sampler arrow (NUTS uses ∇ through the solver) -->
+  <line x1="176" y1="15" x2="10" y2="15" stroke="#222" stroke-width="0.8" stroke-dasharray="3 2"/>
+  <polygon points="10,12 4,15 10,18" fill="#222"/>
+  <text x="93" y="11" text-anchor="middle" font-family="system-ui, sans-serif" font-size="6.5" fill="#555">∇-based NUTS sampler</text>
+</svg>