diff --git a/README.md b/README.md
index 0bba71a..e0b7910 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,13 @@
 # Simuk
 
 Simuk is a Python library for simulation-based calibration (SBC) and the generation of synthetic data.
-Simulation-Based Calibration is a method for validating Bayesian inference by checking whether the posterior distributions align with the expected theoretical results derived from the prior.
 
-Simuk works with [PyMC](http://docs.pymc.io), [Bambi](https://bambinos.github.io/bambi/) and [NumPyro](https://num.pyro.ai/en/latest/index.html) models.
+Prior Simulation-Based Calibration (Prior SBC) is a method for validating Bayesian inference by checking whether the posterior distributions align with the expected theoretical results derived from the prior.
+
+Posterior Simulation-Based Calibration (Posterior SBC) is a method for validating Bayesian inference by checking whether the posterior distributions conditioned on the augmented data (original + posterior predictive) align with the expected theoretical results derived from the posterior.
+
+For Prior SBC, Simuk works with [PyMC](http://docs.pymc.io), [Bambi](https://bambinos.github.io/bambi/) and [NumPyro](https://num.pyro.ai/en/latest/index.html) models.
+For Posterior SBC, Simuk only works with [PyMC](http://docs.pymc.io) models for now.
 
 ## Installation
 
@@ -15,6 +19,8 @@ pip install simuk
 
 ## Quickstart
 
+### Prior SBC
+
 1. Define a PyMC or Bambi model. For example, the centered eight schools model:
 
     ```python
@@ -35,7 +41,7 @@ pip install simuk
     ```python
     sbc = SBC(centered_eight,
             num_simulations=100, # ideally this should be higher, like 1000
-            sample_kwargs={'draws': 25, 'tune': 50})
+            sample_kwargs={'draws': 100, 'tune': 100})
 
     sbc.run_simulations()
     ```
@@ -52,7 +58,74 @@ should be close to uniform and within the oval envelope.
     );
     ```
 
-![Simulation based calibration plots, ecdf](ecdf.png)
+![Prior Simulation based calibration plots, ecdf](docs/examples/img/prior_sbc.png)
+
+We see that due to the funnel neck in the eight schools model, the inference algorithm is not well-calibrated, as indicated by the red points.
+
+### Posterior SBC
+
+Posterior SBC evaluates validity locally, conditional on observed data. It is
+currently implemented for PyMC. This requires storing observed data in
+`pm.Data` containers, using `dims` instead of static shapes, and resizing
+covariates and coords in an `update_data` callback to match the augmented data.
+
+1. Define the model with `pm.Data` and `dims`:
+
+    ```python
+    import numpy as np
+    import pymc as pm
+
+    data = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])
+    sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])
+
+    with pm.Model(coords={"school": np.arange(8)}) as centered_eight:
+        school_idx = pm.Data("school_idx", np.arange(8))
+        y_data = pm.Data("y_data", data)
+        sigma_data = pm.Data("sigma_data", sigma)
+
+        mu = pm.Normal("mu", mu=0, sigma=5)
+        tau = pm.HalfCauchy("tau", beta=5)
+        theta = pm.Normal("theta", mu=mu, sigma=tau, dims="school")
+        y_obs = pm.Normal("y", mu=theta[school_idx], sigma=sigma_data, observed=y_data)
+    ```
+
+2. Sample once to obtain the original trace:
+
+    ```python
+    with centered_eight:
+        idata = pm.sample(progressbar=False)
+    ```
+
+3. Define `update_data` to resize covariates and run Posterior SBC:
+
+    ```python
+    import simuk
+    from arviz_plots import plot_ecdf_pit
+
+    def update_data(model, augmented_data, simulation_idx):
+        with model:
+            pm.set_data({
+                "sigma_data": np.concatenate([sigma, sigma]),
+                "school_idx": np.concatenate([np.arange(8), np.arange(8)])
+            })
+
+    post_sbc = simuk.SBC(
+        centered_eight,
+        method="posterior",
+        trace=idata,
+        update_data=update_data,
+        num_simulations=50,
+        sample_kwargs={"draws": 100, "tune": 100},
+        progress_bar=False
+    )
+    post_sbc.run_simulations()
+
+    plot_ecdf_pit(post_sbc.simulations, group="posterior_sbc", visuals={"xlabel": False})
+    ```
+
+![Posterior Simulation based calibration plots, ecdf](docs/examples/img/posterior_sbc.png)
+
+We see that the funnel neck in the eight schools model is avoided and the inference algorithm is well-calibrated locally for the observed data, as indicated by the absence of red points.
 
 ## References
 
diff --git a/docs/conf.py b/docs/conf.py
index c0901af..fd602ce 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -46,7 +46,7 @@
 numpydoc_show_inherited_class_members = False
 numpydoc_class_members_toctree = False
 
-source_suffix = ".rst"
+source_suffix = {".rst": "restructuredtext", ".ipynb": "myst-nb"}
 
 master_doc = "index"
 language = "en"
diff --git a/docs/examples.rst b/docs/examples.rst
index b813285..b59e976 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -7,13 +7,31 @@ The gallery below presents examples that demonstrate the use of Simuk.
    :gutter: 2 2 3 3
 
    .. grid-item-card::
-      :link: ./examples/gallery/sbc.html
+      :link: ./examples/gallery/prior_sbc.html
       :text-align: center
       :shadow: none
       :class-card: example-gallery
 
-      .. image:: examples/img/sbc.png
-         :alt: SBC
+      .. image:: examples/img/prior_sbc.png
+         :alt: Prior SBC
 
       +++
-      SBC
+      Prior SBC
+
+   .. grid-item-card::
+      :link: ./examples/gallery/posterior_sbc.html
+      :text-align: center
+      :shadow: none
+      :class-card: example-gallery
+
+      .. image:: examples/img/posterior_sbc.png
+         :alt: Posterior SBC
+      +++
+      Posterior SBC
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   examples/gallery/prior_sbc
+   examples/gallery/posterior_sbc
\ No newline at end of file
diff --git a/docs/examples/gallery/posterior_sbc.ipynb b/docs/examples/gallery/posterior_sbc.ipynb
new file mode 100644
index 0000000..de77f9a
--- /dev/null
+++ b/docs/examples/gallery/posterior_sbc.ipynb
@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Posterior Simulation-Based Calibration\n",
+    "\n",
+    "**Posterior SBC** (Säilynoja et al., 2025) validates the inference algorithm\n",
+    "*conditional on observed data*, rather than averaging over the prior.\n",
+    "\n",
+    "```{admonition} When to use Posterior SBC\n",
+    ":class: tip\n",
+    "\n",
+    "Use **Prior SBC** when you want to check that your inference pipeline works\n",
+    "for a wide range of datasets generated under the prior.\n",
+    "\n",
+    "Use **Posterior SBC** when you already have observed data and want to verify\n",
+    "that the inference algorithm is trustworthy *for that specific dataset*.\n",
+    "Posterior SBC focuses on the region of the parameter space that matters\n",
+    "for the observed data, making it more sensitive to local calibration issues.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymc as pm\n",
+    "from arviz_plots import plot_ecdf_pit, style\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import simuk\n",
+    "\n",
+    "random_seed = 42\n",
+    "rng = np.random.default_rng(random_seed)\n",
+    "style.use(\"arviz-variat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How Posterior SBC works\n",
+    "\n",
+    "Given a model $\\pi(\\theta, y) = \\pi(\\theta)\\,\\pi(y \\mid \\theta)$ and\n",
+    "observed data $y_{\\text{obs}}$, Posterior SBC proceeds as follows:\n",
+    "\n",
+    "1. **Fit the model** to $y_{\\text{obs}}$ to obtain posterior draws\n",
+    "   $\\theta'_i \\sim \\pi(\\theta \\mid y_{\\text{obs}})$.\n",
+    "2. **Generate replicated data** from the posterior predictive:\n",
+    "   $y_i \\sim \\pi(y \\mid \\theta'_i)$.\n",
+    "3. **Augment** the observations: $y_{\\text{aug}} = (y_{\\text{obs}}, y_i)$.\n",
+    "4. **Re-fit the model** on the augmented data to get\n",
+    "   $\\theta''_{i,1}, \\ldots, \\theta''_{i,S} \\sim \\pi(\\theta \\mid y_i, y_{\\text{obs}})$.\n",
+    "5. **Compute the rank statistics** of $f(\\theta'_i)$ among $f(\\theta''_{i,1}), \\ldots, f(\\theta''_{i,S})$. Where $f$ is an optional test quantity applied to the parameters before computing ranks.\n",
+    "\n",
+    "By the self-consistency of Bayesian updating, $\\theta'_i$ is also a draw\n",
+    "from the augmented posterior $\\pi(\\theta \\mid y_i, y_{\\text{obs}})$.\n",
+    "Therefore the rank statistics should be **uniformly distributed** if the inference\n",
+    "is calibrated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Out-of-the-box Posterior SBC\n",
+    "\n",
+    "We shall illustrate Posterior SBC using the same eight schools model as the one used in the Prior SBC example. This model is known to have a funnel-shaped posterior (Neal's Funnel) when $\\tau$ is small, which is challenging for many inference algorithms. We use Posterior SBC to focus on the parameter space relevant to the observed data (i.e. the posterior), we wish to see it avoid the funnel neck and show that locally the posterior is well-calibrated. \n",
+    "\n",
+    "### Define the model\n",
+    "\n",
+    "```{admonition} Model requirements for Posterior SBC\n",
+    ":class: warning\n",
+    "\n",
+    "Posterior SBC augments the observed data (concatenating original + replicated),\n",
+    "which changes its size. For this to work, store observed data in ``pm.Data``\n",
+    "containers, and specify size using the ``dims`` parameter instead of setting a static shape. \n",
+    "If your model uses ``dims`` and ``coords``, you are also responsible for resizing them to the correct size corresponding to the new augmented dataset via the ``update_data`` callback.\n",
+    "Similarly, if your model has covariates, store them in ``pm.Data`` so they\n",
+    "can be resized in the same callback.\n",
+    "```\n",
+    "\n",
+    "We define the eight schools model as before, but with the observed data stored in `pm.Data` containers and sized using `dims` to properly handle the augmented dataset. Here we also add `sigma` and `theta_idx` to the data containers so they can be resized in the `update_data` callback."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])\n",
+    "sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])\n",
+    "\n",
+    "coords={\n",
+    "    \"obs\": np.arange(8),\n",
+    "    \"school\": np.arange(8),\n",
+    "    }\n",
+    "school_idx = np.arange(8)\n",
+    "\n",
+    "with pm.Model(coords=coords) as centered_eight:\n",
+    "    school_idx = pm.Data(\"school_idx\", school_idx, dims=\"obs_id\")\n",
+    "    sigma = pm.Data(\"sigma\", sigma, dims=\"obs\")\n",
+    "    data = pm.Data(\"data\", data, dims=\"obs\")\n",
+    "    \n",
+    "    mu = pm.Normal(name='mu', mu=0, sigma=5)\n",
+    "    tau = pm.HalfCauchy('tau', beta=5)\n",
+    "    theta = pm.Normal('theta', mu=mu, sigma=tau, dims=\"school\")\n",
+    "    y_obs = pm.Normal('y', mu=theta[school_idx], sigma=sigma, observed=data, dims=\"obs\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fit the original posterior\n",
+    "\n",
+    "First, we need the posterior samples from the observed data. These will\n",
+    "serve as the reference distribution for Posterior SBC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with centered_eight: \n",
+    "    trace = pm.sample(1000, tune=1000, random_seed=random_seed, progressbar=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using `update_data` with covariates and `dims`\n",
+    "\n",
+    "When your model uses `dims`/`coords` or has covariates stored in `pm.Data`,\n",
+    "you must provide an `update_data` callback that resizes everything to\n",
+    "match the augmented observations. The callback is called **before** the model\n",
+    "is re-conditioned, and runs inside the model context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_data(model, augmented_data, simulation_idx):\n",
+    "    with model:\n",
+    "        pm.set_data(\n",
+    "            new_data={\n",
+    "                \"sigma\": np.concatenate(\n",
+    "                    [model[\"sigma\"].get_value(), model[\"sigma\"].get_value()]\n",
+    "                ),\n",
+    "                \"school_idx\": np.concatenate(\n",
+    "                    [model[\"school_idx\"].get_value(), model[\"school_idx\"].get_value()]\n",
+    "                ),\n",
+    "            },\n",
+    "            coords={\"obs\": np.arange(16)},\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Custom test quantities with `transform`\n",
+    "\n",
+    "You can define a scalar test quantity applied to both the reference draw\n",
+    "and the posterior draws before computing the rank statistic. The function\n",
+    "receives `(param_name, param_value)` and should return a comparable value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def transform(param_name, param_value):\n",
+    "    return param_value**3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run Posterior SBC\n",
+    "\n",
+    "Pass `method=\"posterior\"` and provide the `trace`. In each iteration, Posterior SBC\n",
+    "generates replicated data from the posterior predictive, augments it\n",
+    "with the original observations, and re-fits the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sbc = simuk.SBC(\n",
+    "    centered_eight,\n",
+    "    method=\"posterior\",\n",
+    "    trace=trace,\n",
+    "    transform=transform,\n",
+    "    update_data=update_data,\n",
+    "    num_simulations=50,\n",
+    "    seed=random_seed,\n",
+    "    sample_kwargs={\"draws\": 100, \"tune\": 100},\n",
+    "    progress_bar=False,\n",
+    ")\n",
+    "\n",
+    "sbc.run_simulations();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize the results\n",
+    "\n",
+    "If the inference algorithm was well-calibrated, we expect a uniform distribution that lies within the 94% credible interval, indicated by not having any red points. In our case, we wish to see that the funnel neck in the eight schools model is avoided and the inference algorithm is well-calibrated locally for the observed data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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48eNLtv18xTGkwvdPP/10cjECAFQ6tW5u4kW6qf2u2MVvyy23zLh8x44dw/Dhwwtax7z77rsNbmfq7KLWVetmS60LQCVT05ZfTRuPm8Vp2GOX69g98Oyzzw75UtOqabOlpgWg1NSllad+A7BMDdsidam6NFvqUoDqVBPh6jfffDO89dZbdbc33HDDsOuuu2Z8TuzaWn86kOjuu+8u2JjyCVrGLg+xm3T9bhOFDHyz0k033RR+97vfJYX0euutF772ta8lnTyGDh1a9xLNmDEjnHDCCWHChAll8bLVD1vEaXRiGL85m2++ed337733XihHsat0yh133JGEmNO588476/7O48H72Jm5KfH3Wr9LS+zinY0Yqr/wwgvrOl4PHDgwHHDAAWHPPfcMvXr1Su6Lj51xxhlJd+sYJP7oo49WuUqxVGK39fi+ber3n079ZaZNmxbmzZuX9/bjVKCpndO4w5rr9kv1Ho0X4aQugvnss8+SjvYAUMnUui2rtTfZZJPQvn37Vq1jYgjmkksuqbsdQy59+vRpclm17qqvfza/I7WuWheAyqKmLc+aNjamSB07i9PHNzVTZzbUtKu+/tn8jtS0aloAWp+6tDLVzw717ds37XLq0pXUpdmRKwCoTs0fQasC9a8mS01Hl43YhfbSSy+tu/3YY4+FM888M5RS7MY7ZcqUutvTp08v6Xiq0W9+85vQqVOn8Mtf/jLsv//+DR6Lgdkf/OAHyfQwsWtxfD/861//SpZvLAaB4w5VIQ0bNmyVMaWCq9l0sauv/nLlGq4+9NBDw9///vdkBydeTHDwwQeH4447Luyxxx7JhQYxzDxp0qSka3Tq4ocePXokQej4b1Piz5oKCccQdrpgSn3PPfdcuPbaa+tuf+973wunnHJK3VWtcWrM0047LekMvWDBgmTqy86dO9ctnylc/Ytf/CIUUvyZ4lgyvUeyfZ80Xia+dvUvMshF/fdYnNa+qb+ZTNuPr/HMmTPzPinTEvGEU2onO34GxL9DAKhUat3c5VNr15+BJZ9aOwaq44VxL774YrjhhhvqLqqNXQbPOeectM9T62b/e1LrrqTWBaASqWnLr6aNzSZiw5Jo2223DQcddFAeo/zfthy/VdPmQk0LQKmoSytPDEzHfEf9BofpqEtXcqw1e+pSgOpTE+HqZ599tsHtbbbZJuuDh7HDdAzSpg5Afvjhh1kffCyGdu3aNbi9ZMmSko2lWsXX9LLLLmuy83EMVcbg7MiRI5MQbXxP3H777eHII49cZdnYKeKWW24p6NhioLupcHUMntYPrmZjzTXXrPs+/iwxwBGneywnMYB788031wWXY/flK6+8MvlqrEOHDkmn69NPPz1stNFGGTvZ1+9in42rr746rFixIvl+p512Cqeeeuoqgea487z33nsnr+UjjzySjCfaYIMN6jofN6XQ75H4mdVUuHrWrFl133fv3r1B+DtTB/9u3bolwfZU5+Z8tfQ9mlpHKcLV9d9PZgsAoNKpdVunjomzyaRkW0PFDiBxKvV01l9//XDFFVckF9ymo9ZV6+ZKrQtAJVLTll9Ne+655ybHReMx0fPOO6/BVOu5UtOqaXOlpgWgVNSllefWW2+tu3Cwbdu2q8xmX5+6VF2aK3UpQPVpG2rAO++8U/d9LJC22GKLrJ/buENp/XWVQuwAkSl8SMvFzhpNBatTYlj22GOPrbsdOyuXWgxdp2QTmm1quVSAttzE4PKNN94YrrvuuiQ4nM6QIUPCvvvum/x+Mqnf+T3TND8p8eKK2DEw5YQTTmhyubiunXfeOfk+dhmMF2JEe+65ZygH+bxHGi9bfx2tsf3Gy7Vk+y1Rv7t5/fcPAFQitW7r1DH1Z+loaZ0d92GPP/74cO+992YMVkdqXbVurtS6AFQiNW151bSx+cjzzz9fd+w0U+OLbKhp1bS5UtMCUCrq0sry9ttvN5i5Ps6aHTsNp6MuVZfmSl0KUH2qvnN17Kgwc+bMBl0ZYjfWbPXv37/B7dipeJdddgmlEEOe9bumtm/fvtmT643F7r9xHW+88Ub49NNPkxP1MbwaQ6ux+I+df7fccstQy5rqDN1Y7Fx97bXXJt/H1zK+z3r27Nlgme23377B1YzFtGjRorrvUx2Tm9O4S3X9dZTbFb9//OMfw1/+8pfkdY5ToW+99dZJZ/nYbTvuBL366qth3LhxyddNN90UrrrqqrQXHnzyySd138f3fnPGjx9f933smhz/TtKJnw0PPfRQg/v22GOPjOsv5/dI4/fJwoULS/oebcn2W6JXr15Nvn8AoBKpdVu/jsm2zj7iiCOSaSmj2PXvo48+ChMmTEiCLLEefvDBB5OOgJn2R9W6at1cqXUBqERq2vKpaWP9GWf0S8208t3vfjeP0a26zhTHb3P7PTl+6/gtAK1LXVo55syZE0455ZS6iw7XW2+9cNZZZ2V8jrrUsdZcOdYKUH2qPlw9efLkBrdjIDMXjTvbNl5fa/rrX/8aVqxYUXc7BkxXW221nNbxn//8Z5X7YgEZu+zGx6655pqw4447hrPPPjvjVXrVbPjw4c0uEw8UxwO7cTrF+DuJgfUvfvGLoVTqdxGJYeRsxGByunWUixgqid1OxowZUxc4Of3000OPHj0aLPfaa6+FM844I7z77rth7Nix4Tvf+U647bbbVgnnptaZS5eWuM6UeOFBvCAhU/fsxp83Q4cODeUgn/dI4/dJLh2vi/Eebcn2W6L+duu/fwCgEql1W7+OybbOPuecc5rcV7vlllvClVdemeyLnnjiieGCCy5ILvZsilpXrZsrtS4AlUhNWz417S9+8YukIUbq+6aOx+ZKTaumzZWaFoBSUZdWhnih4MknnxwmTZqU3O7evXtyvLVbt24Zn6cuVZfmSl0KUH3SpwSrxLx58xrcjp1n872yKJo7d24ohRjwjOHq+o499tiibOu5554L3/jGN8J9990XalG2Afz6y9Xvjl4KsZtzrt0pGi/X3M5DKVx44YV1weoDDzww/L//9/9WCVZHm2++ebjxxhuTzvRRDLvH24WQOjkQ9evXL+OyG2+8cYPOL811rS7390jjZeuvozW233i5lmy/Jepf1AIAlU6t2zp1TP3Ofi3d9vHHHx9++9vfJrdjZ+tYE3/wwQehENS6al21LgCVSE1bHjXtI488ksyukjp2G2dyLAU1rZpWTQtAqahLy9/SpUvDqFGj6poQxosGY8PBzTbbrODbUpeqS9WlANWn6jtXxymUW9Kdt3Gn1NQ0Ia0pdoj44Q9/2KBTROySvPvuu+d08PRLX/pS8rxBgwYlIfP27duHWbNmJV1/H3roofDCCy80uAovdgKOnbF32mmnvMf+8ccfZ3w8br/cdOnSJeflGr/PWlv96RE//fTTrJ5Tfxqb+LMUoqtIIU2bNi3cfvvtyfdt2rRJdnoyWWuttcIxxxwTLrvssuT2HXfckQRRMv3esjmRUP+K1ObeG/E1jH9f8W8qGjFiRCgX9S8UiRedxJMjzX0exp+9/nu7Z8+eJXuPNl5Ha6p/IinbzwcAql8l1rmRWrd16pgZM2YUpIZKiXXlDjvsEJ5//vmkNrn11lvDj3/841WWU+uqdXOl1gWobWra2jl+W+iaNh43PO+88+qOO5555pmhUNS0atpcqWkBKp+6tHbq0tYUG1WcddZZ4bHHHktux3zMFVdckfVFgepSdWmu1KUA1afqw9X1g5FRroVe4/Bh4/W1htiZLHbird8l4vzzz886VP2zn/0smTa6qa6vG2ywQfjCF74QjjrqqKRjdQxUp4roZcuWhdNPPz0JXucbatx1110zPh67/BbjqsCWiL/jOBVMNstl6tzx3//+N9x0000FHduwYcPC/vvv3+TvMeXDDz/Mal31l9twww1DuYnvx/geTP18ffr0afY58eKBlPfeey8JBjf+3cQQdi6hp/o7TfUvcEgnNa1m3DnbZpttml0+TpdZSPFv9bTTTsv4HommTp3a7O+98XupJe+T+s+NnzHZhLvrbz/+XLnOPFAo9TvT13//AFDbKrHOjdS6ucun1v7oo48KXmvvuOOOSbg6evnll5tcRq27klo3e2pdgNqmpq2d47eFrmnj8b3p06fXNcY48cQT066n8THVU089te48TXwPnnLKKQ0eV9OupKbNnpoWoPKpS2unLm1NP//5z8Pdd9+dfN+2bdtw0UUXha985StZP19dupK6NHvqUoDqU/Xh6sbigb6WLN/a0zj88Y9/TDrw1h9PDFYPGDAgq+fHMOKRRx6Z9cn6m2++ORx66KFh7ty5dVOX/PnPf046Z9eKeNA4dh/OZrmmugLX77x8yy23FHRssXN6UztBG220UYOOIvGruRBoqrtyue4ExdcvJdtwf+PlYofmxuHqddddN+uroKPYvT3bq3fffvvt5Cs1xVD8Gfr165fxOYV+j8Sfr6lwdY8ePZL3RKrjTLxgo7nfe/33SAy3Z3PRQTpxRz3utMYrhOPnaNz+8OHDK+I9Wv+9WP/9AwCVSK2bu/q19ltvvZXUefFCutauY+p3C5w9e3aTy6h11bq5UusCUInUtOVV08YAQf0QQXPqN5Jpar1qWjVtrtS0AJSKurR8XXDBBeFvf/tb3e0468rXvva1nNahLlWX5kpdClB92oYq13ja6/rTMGRj4cKFDW431f25WP71r3+FSy+9tMF9sbP0V7/61aJtMx5k/dGPftTgvttvvz3vUPmTTz6Z8evvf/97gUZeOOPGjWt2mdiVOhVoiIH3zTffPJTS+uuvH/r27Vt3e8yYMc0+p/4y9Ts+l4vOnTvXfR9D/tloHDKpH4xO2XTTTeu+f//995td58Ybb9zgxEMm9957b4Pbb775Zign9ac4euGFF5pd/j//+U/B3iOxS3W8QjqX92ght98SsQt6Sjl2IAWgNCqxzo3Uurnbaqut6jrrxYsdJ06cmHH52Jmv/utcqDom07TsKWrdldS62VPrAtQ2NW3tHL8tl5o2G2raldS02VPTAlQ+dWnt1KWt4bLLLgs33nhj3e2zzz47fOMb38h5PerSldSl2VOXAlSfqu9c3TgMnWu4uvHyrRWujjsQP/nJTxqEmo8//vjwne98p+jbPvDAA5NQdyrQGrtOxJBo/eIxW/UL83TTpZebGGo/5JBDMi5z55131n0fX5emwg0xyNpa4doY8N5tt93CrbfemtyO3c733XfftMuPHTs2CYhHsZtwfG65WWeddRoUofEqv9g9OZN///vfdd/HLs2NL65IdVCOXZxjd/YYxm5uvVtuuWXd9++++26YNGlSGDhw4CrLLVu2LHnv1PfGG280O7VQawaw99hjj3DPPfck399///3JZ0z9EHvjC0viMvWfW4jtx/de6j16wgknpF02dhVPTXtfqO3nq36ovn5AHIDaVol1bqTWzV2cCWWHHXZI9tFSdUymGTgeeuih8Pnnnyffx/2EbbfdNhTCE0880WTnwfrUumrdXKl1AWqbmrZ2jt8Wuqbt379/1q/dlClTwu677153+9FHH02en46aVk2bKzUtQOVTl9ZOXVpsv//978O1115bdzvO+PzNb34zr3WpS9WluVKXAlSfqu9c3b179wa3Z82aldPzG09nF0OZxfbiiy8mRV6cmi8lXkkXu1a3hhgEaXywtLmOvdUkdsu977770j4edx7qX+nYXBC7tRx22GHJDk30zDPPhGeffbbJ5ZYvXx4uvvjiutv77LNP6N27dyg3MZzerl275Pt4kcEVV1zRbCe/m266qe72zjvv3ORy8TWqf0XtSy+9lHG9AwYMaBCwvvLKK5tc7u677w4ffvjhKn/L5SSexEgdnJgzZ06yc5nONddckyyTmvLoy1/+cou3P3LkyLoLVGLX8EwdPeN7NAbWU511hgwZEkohXmTyzjvv1J1I2mKLLUoyDgAoFLVufo444oi67+MJh7fffrvJ5RYsWNCgXjz00EObnG49dgvM5cLfeLLjlVdeqbu95557NrmcWletmwu1LgCVSk1bHjVtsahp1bS5UNMCUErq0vISMxyXX3553e3YuPCUU07Je33qUnVpLtSlANWp6sPVjTvMfvTRRzk9P3ZPbRy0LKbXXnstfPe73026xtYPv5533nmhNcUwZaaQeTWL4fKzzjprlS7E0YQJE8K3vvWt5ABzatqcgw8+OJSDwYMHh69//et1t08//fRVpmiJIY4f//jHdYHi+LN+//vfb3a9qa940L21rL766uGAAw6ou/2Pf/wj+TuIHacbe/XVV5MrTj/99NO6HZ34e0qnfjfp+t2u06m/rtj5OU4llAr+pjpU/+pXv1rlcyIG3J977rlQLuLUn9/73vfqbv/xj38MN998c4MO+TF8H3c842Mp8WKP1LShTTn66KPr3iPxbyedNdZYo8GVweeff/4qFzLEKUcvueSSug7bqfdyqcRpruJrkgrspwL/AFCp1Lr51brxQrNtttkm+X7JkiXhxBNPXKWbTLyQNx6sjzOdpOrZOPtQU+IyI0aMCH/6058y7qPGCwgvuOCC8Itf/KLuvjiOTLOjqHVXUus2T60LQKVS05ZHTVtMatqV1LTNU9MCUErq0vLIFUS33357uPDCC+tuH3nkkeFHP/pRi9erLl1JXdo8dSlAdWqzon6qrkrF6e5S4eAYunz55ZdDly5dsnruD37wgwbhv1g07LLLLkUZ53vvvZcUefWDzDHMF7vLtva04r/97W/DH/7whwbhxniwtdBiGDaGyaM4PUsMX5ZCLPBTfvrTn9YFZddbb71kesT4+sfutePHj69bLr6H/vKXv2ScPrG1zZs3L+lgXb/rSBxfnDY8PhaDxPGKuZRf//rXSTfhbF+buENy4IEHpl02HmifPn16g/s++eST5CuKXYvja9rYddddF/r06bPK/fFg/uGHH550OU6J69h6663DOuusk5wEiF3VY7i6vrijFK9ETScGtL/0pS8l3QLXWmut8NRTT9V1/U4nvk8ff/zxBhcgxA7v8XWNz4+h4NTFEPEz4uyzz657n8T7evXqFc4888xQDuI46l88EC9CGTZsWBKyHjduXPjggw/qHou/7/o7ounC1XFnIYrvp/i+Sif+zuLvpn6ofZNNNkk6U8ffR7zCO4aIUmIY/NRTT824/TiVaFMdxWPoPSW+71Jds1PitFXNXVxQ/7WK79Ndd9014/IAUG51bqTWLUytGy+8jRdWpmqVWD/GejBeWBf34Z5//vm6izBjZ78YnI77ok15/fXXG1xIGGvLWBPFmjHue8Qp2OP+YQy71L+oL05FGWdrWXvttdOOU62r1o3UugAUgpq2+o7fFrKmzdaUKVOSGfXqH8vr379/xueoadW0kZoWgBR1afXVpYXMFcRjqPFYa6phVnxuvN2mTZuQjdhoLF5U2BR1qbo0UpcC1K7Wm8uthDbeeOO64F8sqCZOnJgcMMxG/TBtal3F8OGHHybdcesHq2MXiauuuqrVg9WpUGt96YrJanTMMcckOwpXX311mDx5cvLVWAzkXnrppWUVrI66d+8e/vznPyeB0FR4NYZl41d9cYfinHPOaXYHKFfvvvtumDp1atrHY+fs+oHX+oHbpsRwSeys/JOf/CQJMKfW8fTTTze5fPy5YufkOFVlJj169Ah77bVXuOuuu5ITCTHQu/3222d8TpxCKF5kEA/+R/HnbPyzbrbZZkl37W7duoW///3vyYUc8WREvDI3brNcwtWxY3Qczy233JIEqmMnmlQ3mpS4s3nUUUclnc4LKX6exc+1c889N9x///3JfTEgH78aLxdD1alQWibx77Wp91V9Tf0dx99XJjHsnQrUx530GMgHgEqn1s1f3759kxk+fvjDHybh6LhvGWeKaTxbTO/evZOTB5lCKDGoEoMsqQP+TdWW9cVlYwjmjDPOCD179sw4TrWuWjdS6wJQzdS05VHTFpOaVk0bqWkBKHfq0vLIFcyePbvuOGvqubfeemvWY4k5nXR5GHWpujRSlwLUrpoIV++444514eroxRdfzCpcHbs41C/oYpewfv36FeVKy+OOO67BdNCxi2vsHN25c+dQCq+88kqD25k6o1Wj2C03dh8ePXp0eOmll5KrJmMAIl4ducceeySh09VWWy2UoxgCjR21H3744XD33XcnXZ1jgDgGj+P7N05dE4MZxXgvF0MMsseO8TEgHn+e+G/sdhK7+cXfSdzRiV3+4t95DIs3FzZJOfbYY5NwdRSD0M2Fq+Pf4jXXXBOefPLJ5Hljx45NLoaIHavjyYb4/Nj1PLX966+/PummHJdNXWFbLjp27JiEm/fff/9kiqT4+Tht2rS69892222XvEeGDh1alO3HndAYVv/GN74R7rzzzuR3Gt+j8fcZO5LvtNNOyfbjldGl9MADD4Q5c+Yk38dZBeL4AKAaqHXzF+uT2267LZnd6J577klmt4m1Xtw3iN3+4r7CQQcdlNSHmQwaNCg8++yzyVesK2N3lTh7SDwRsHTp0uRivVSd+4UvfCF87Wtfa3Kml3TUumrd5qh1Aah0atrS17TFpqZV0zZHTQtAOVCXVj91qbq0OepSgOrVZkVsW1rl4onq/fbbr8HBw3jgsDlxSpHYnTjl29/+dsE7z8ZpRI4++uikS0T98f31r38t2cHLeJXgvvvum3S0jdq1a5dMB5htaLUSp/CpP0VNfL9QG+JFDc8991wSNo4ditdcc81SD4kyEqfCimGnGAaPHcuL8RkIQPUqlzo3UuvWJrUumah1AciGmpZSU9OSiZoWoHaoSyk1dSmZqEsBqlfbUANimCB2/KofHo7dZzNZuHBh0rW4vtgtrJDiNk488cQGwer+/fuHG264oWTB6hiovvjii+uC1dHw4cOFCqlK3//+95N/Y/fpm266qdTDoYz85z//SYLV0Te/+U2fgQBAxVHrko5aFwCoFGpa0lHTAgCtSV1KOupSgOpWE+Hq6NRTT21w+5e//GX47LPP0i4fO1ZPnTq17nacCm/zzTdPu/wdd9yRhLhTX7EbdSZLlixJpoh56aWX6u5be+21w1/+8pecpnvOZPz48WHcuHFZL798+fJw0UUXJR1860t13INqEy8c2GuvvZLvb7nlljBr1qxSD4kycfXVV9d9LscrkQEAKo1al3TUugBApVDTko6aFgBoTepS0lGXAlS3mglX77nnnmGrrbaqu/3BBx+Eo446Krz55psNlps7d24SvK7fxbZTp05h1KhRBR3PWWedFZ566qm62507dw7nn39+aNOmTZgyZUrWX3PmzEm7jdih+9BDDw3HHntsEv6eOXNmk8vFLtXxaqq4XOyaXd+IESPCLrvsUsCfHMpL/Fvs0qVLmDdvXvj9739f6uFQBp5++unw/PPPJ9//6Ec/Ct26dSv1kAAA8qLWpTG1LgBQadS0NKamBQBKQV1KY+pSgOrXPtSIGFq+4oorwsEHHxymT5+e3PfWW2+F/fffPwwZMiQMGDAgzJ49O0yYMCF8/vnnDZ4bQ8+DBg0q6HjuueeeBrcXLlwYTjjhhLw6cscO2Jn8+9//Tr6iddddN6y//vqhR48eoX379snP/NprrzUZvB42bFi4+OKLcx4TVJJ+/frl1OGd6rfzzjuvcuENAEAlUuvSmFoXAKg0aloaU9MCAKWgLqUxdSlA9auZcHXUp0+f8Oc//zmcdtpp4f3336/r2jxx4sTkq7HYsTpefbbffvuFajF16tTkqzmHH354OPvss5PXAAAAAAAAAAAAAABqQdtQYzbZZJNw5513huOPPz6sscYaTS7ToUOH8JWvfCX8/e9/D0cccUSoVFtvvXX45je/GTbbbLOkS3VzunfvHkaOHBn+9a9/hZ///OeC1QAAAAAAAAAAAADUlJrqXJ3SpUuXcMYZZ4RRo0aFl19+OUyZMiV88sknoVu3bqFv375hq622Cr17985pnQceeGDyla0333wzFNvAgQOT7tPRokWLwttvv510rZ4xY0aYP39+WLZsWejRo0fo2bN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+      "text/plain": [
+       "<Figure size 2880x1148.7 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_ecdf_pit(sbc.simulations,\n",
+    "              group=\"posterior_sbc\",\n",
+    "              visuals={\"xlabel\": False},\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Intentionally Skewing the Augmented Posterior Using Custom augmentation with `augment_observed`\n",
+    "\n",
+    "We intentionally skew the augmented posterior by keeping only 1 of the original observations and concatenating them with the replicated data. This creates a mismatch between the reference draw (which is based on the full observed data) and the augmented posterior (which is based on a subset of the observed data), leading to skewed rank statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def augment_observed(model, observed_data, replicated_data, simulation_idx):\n",
+    "    \"\"\"Keep only the last 25 original observations + replicated.\"\"\"\n",
+    "    data = {\"y\": np.concatenate([observed_data[\"y\"].values[-1:], replicated_data[\"y\"]])}\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def update_data(model, augmented_data, simulation_idx):\n",
+    "    with model:\n",
+    "        pm.set_data(\n",
+    "            new_data={\n",
+    "                \"sigma\": np.concatenate(\n",
+    "                    [model[\"sigma\"].get_value()[-1:], model[\"sigma\"].get_value()]\n",
+    "                ),\n",
+    "                \"school_idx\": np.concatenate(\n",
+    "                    [model[\"school_idx\"].get_value()[-1:], model[\"school_idx\"].get_value()]\n",
+    "                ),\n",
+    "            },\n",
+    "            coords={\"obs\": np.arange(8 + 1)},\n",
+    "        )\n",
+    "\n",
+    "skewed_sbc = simuk.SBC(\n",
+    "    centered_eight,\n",
+    "    method=\"posterior\",\n",
+    "    trace=trace,\n",
+    "    augment_observed=augment_observed,\n",
+    "    update_data=update_data,\n",
+    "    num_simulations=50,\n",
+    "    sample_kwargs={\"chains\": 4, \"draws\": 50, \"tune\": 50},\n",
+    "    progress_bar=False,\n",
+    ")\n",
+    "\n",
+    "skewed_sbc.run_simulations()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize the skewed results and and Replot the Original Simulations using `compute_rank_statistics`\n",
+    "\n",
+    "The results indicate a clear deviation from uniformity, with the ECDF lines falling outside the credible interval, as indicated by the red points. This suggests that the self-consistency property of Bayesian updating does not hold."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<arviz_plots.plot_collection.PlotCollection at 0x1fdf7f76660>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2880x1148.7 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_ecdf_pit(skewed_sbc.simulations, group=\"posterior_sbc\", visuals={\"xlabel\": False})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We shall also replot the original Posterior SBC results using the same quantity using `compute_rank_statistics`. This allows us to compare the results without need to re-run the simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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46eRcyJBx4zH94Ac/WCVYXV8MRN93331h/PiVf/SvvvpqePTRR8Mee+yRMVT+5ptv1t0ePnx4OOaYY9IuP2DAgGQcv/jFL5LbMZQdx3n11VeHarNsxYowZ3FuwdJyvkrFlTYtV2s7ocX+efMNcJfz3xkUS2vveMftxL/NWvrMAwAAANJ3ic4nzFCMC7jLaSwAAEDxtVY39mLkEMoxHK4RHuTHcQVqNlwdw8VvvfVW3e0NN9ww7Lrrrhmf06VLl3DYYYeFSy+9tO6+u+++u2Dh6s8++yw8/fTTDbpWH3TQQRmfEztUx4D16aef3mBMmcLV99xzT4Pb8fmZOl1HBx98cLj88svDnDlzkttPPvlk0sm6R48eoVo8OuXTcPG498OsLFv/V8JVKtleaRONGjow7J3hZ8g2qA3NqbXAOlTCjjcAAABAfTHEkO/xiEJfwF1OYwEAAFpHJecKyjEcrhEetaqlTe0cV6Amw9WPP/54g9v77bdfVs/7+te/3iBcHTtZn3nmmQUZ01NPPRWWLv3ff6x77bVX6NSpU7PPi0HqGPxesGBBcvuZZ54JixcvDh07dlxl2Xh/fDyla9euGYPYKXEcI0aMCP/4xz+S20uWLEnGu++++4ZqccHL7+b1QVotV6lcPmFS8gVAbex4x4tmcrkIBwAAAAAAAIDKDIeX45ig2DS1o9BqIlz97LPPNri9zTbbZPW8ddZZJ6y77rph6tSpye33338/fPjhh6Ffv34tHtNzzz2X15hi8HnLLbcMY8aMSW7PmzcvTJgwocnnjxs3LsyfP7/udnxeUyHspsT1pcLVqdewmsLVLVEOV6m09EobAMqTnVwAAACgHIweMSys3sQx8FJcwF1OYwEAAACqo6md4wo0pybC1e+8807d923btg1bbLFF1s8dNmxYXbg6ta5ChKvffvvtBrdj8DmXMaXC1al1NRWubryNoUOH5rSNdK9hNfjJFzYKF497P8xatCRUota+0iYGuWOgGwAAAACA6hfDzOXS5aycxsJKS5evKPq058WaIh0AcrFax/ZJHZLKFcTv430AQPnS1I5Cqfqq77PPPgszZ86su73GGmuELl26ZP38/v37N7gdu1fvsssuLR5XXE9KmzZtwoABA1o0pua2ERVjG5Vq9/5rhC+v2zvMWVy5V6nkcqVN9MDkGeHyCZPyClbHILcDkwDVJf7/lg0npwAAAACqWy5h6XzPNRRT6jxGPG8CAIXUrk2b8KPhG4QLXn43uR2/j/cBAFD9qj5cPXny5Aa311lnnZye37dv34zry0cMe8+bN6/udu/evUPHjh0LPqYPPvgg4/My6dSpU+jVq1eYNWtWcnvOnDnJ9/G+ahF3eiq920UuV9ocPqhfOGSjdXLuJiFUB1Cdsr1wyMkpAAAAoFIu4C6nsVSK+yfPaLVZMosljj3+DLEhjd8rAMVo3Ba/AIDq57gCNRWurh9iTgWZc9E4TDx37tySj6nx8unG1Pj+fLaTClenxl1N4eoFCxZktdzCRauGkZcuXRpCmlBztuvNtP4ml1u4MKxYsSLko127dkmAv6kw9qJFi8Ly5cvTPnfJoqUh3eHotm3bJkH8pjS33kwyrXfx4sVh2bL8DvLGLvGdO3du8rElS5as/L3mKV1H/LjOuO5Crze+BvG1yFd8HeLrUej1xt9b/P01Ft8L8T2Rr/j+je/jxuLfRPzbKPR68/lbrq9Dhw6hffv2BV9vXGdcd7E+I1rzb9lnROt+RmT7f01zJ6farFjuM6LKPiNiZ6rmTp62bde2wWdEnPJQZ47yVy3v0Wz4f2wlte7/qHVXUuv6jKjPZ4TPiFr8jMi11lXnlp9qeS8WuqbN5r2d0rZtm9Ax3XHWZL0rx/tZEw05Fi5YGBYsb+L+Jo4xZHsBd7f2bcP3h/QPe67b9LmK5Hhlm3ZZH79tyVhSF5OP6Nerpo/fxvdTpQerU+LPMGPOvLB6p/Y1/RmRC8dv/8c5npWc4ym/zwg1bXlQl67k/5yV5ApWkivwGdGYzwifEZX8GdHSYxzNHWvN5VhOfb26dg6dOxYmn1ifXEH2qj5c/fnnnze4nS7klU7jIOb8+fNbPKbG68ila3VTP0O6MTW+v6U/e+PXMhsff/xxxsfrh7db2/XXX5/VcgvbtA+hz9AG9738+lthuy03b3L5W/7vb2HRwuw/yBe2bR/CWk2vq7477rgj79driy22CLvuumuTj913333hww8/zGu9G220Udh7772bfOzxxx8P7767cnqkXPXr1y+MHDmyyceef/75MHHixLzWGy8OOOKII5p8bOzYseE///lPXuuNfyvf/va3m3zs9ddfD0899VTI1ymnnNLk/e+//3548MEH817vt771rSYPFsa/2X/+8595r/ewww4La6yx6pXb8b07evTovNd7wAEHhHXXXbfJg03Z/i03Za+99gobb7xxk4+1ZL277LJL2HLLLZt87NZbb807EL7tttuG7bbbrsnHfEas5DOi6c+IeHqlw9pDw5L4f04eYqEfZz6Y/8l0nxFV9BnxXufe4YXV+uf8vogXasWpD2u9U0c517nV8h7Nllp3JbXu/6h1V1Lr+oyoz2eEz4ha+4zIp9atxTpXTVt5NW2++3H5iH+znVcszep4ebY+X7o8XDz2vTD5wX+Gtmn+v9rnoEOyPn7bkrGkLiZfZ/ZH4dmnnw61evw2Hu+phmB1c+/bcv3/Klv2e1dS0/6P/d6V1LTVU9OqS3Pn/5yV5Aqqpy5NkSv4H3XpSrJHK8keFfczoqXHOPbJcKz1nFv/mfexnC5tQ/jx1huHfdZbq+KOtVZybVpT4erGKf2WBplbkvovVOg523B147Hmup3Gr1U+P3u6A8Ip8erczTbbLFSan02aH8KkF5t+sOfgEHq29ogAIL14wnL7OVNa7QQs5S8G7vN9P8xatCRc8PK7Fb0TVAjVWucCANRqrVuLda6atnb24wqp44qloVNYHhY1GY9uXhz/4jbtswrAZjOWDsuXtuhi8vnVkysuiW3mTAkbLpgZ9vnqPmGdddZpsgN6POGbr6/s9pWwwQYbNPnYNTfcFO7KonENQCWqtZpWXQoAlEKPDu2Tma3yveg4dYwjndixuiXHchYsD3WzjLdvu+rsVq1heY3nCmouXdPUNGq5LJ/vlF25bKPQy5fzzw4AtI4NF84M6y+cmbG4T9n3kG+EY596o1XGRWnE90GpT8gDAEAxqHWpVq353o6B5RhcbkqMVI9oMyc80X6Nknc8djF5cew347XQeXnz4ff4HklF7Ht2aJd0pGpswfKlLQrSr9a+bZPrjbIZI0ClUtMCABRfDCyfMXyDpAN17ERdaHG2qJYey0nNMp5u37jYFtd4rqDqf/LG01MsWrQop+c3bp/etWvXFo+p8TpybdGe7Zga/+y5bqfxa5XPz/7kk09mfDy2kf/Zz34WyllLu1/kKl4RE6+MAYBCiye8sjmh1dP/Q2QxfU+tq4Y6FwCA2q5z1bQ0JR4Lj7NfZepLvUWbheHsr22bnNxr7LnnngtvvP6/C7YXtm1f1O7C2VxM3qlzp3DkkUeG2YuWhMMeHl+0sVSLGFouRGdxAFpfpda06lIAoFT2WW+tsNMa3cJ1N97c7LLFPsZRbXpVaG1aX5sVVd6OOB7IO+644+puf/nLXw5/+MMfsn7+/fffH0aNGlV3e//99w+/+c1vWjSmSZMmhT333LPu9qBBg8I999yT9fMnTJgQDjnkkLrb22yzTbjllltWWe7www8PL7/8ct3t22+/PWy55ZZZb2ffffcN77zzTt3tRx55JAwYMCAU0qeffhq++93vJt9fe+21YY01Wq8N/IIFC7Je9qGpM8MVr04pylUq9XVv3y6csdUGyQd3YzEcn++fa7t27ULHjh3ThuiXL8/v52rbtm3o1KlTq6538eLFYdmy/DqixG7snTt3bvKxJUuWhKVL8z9g3PhihpS4zrjuQq83vgbxtchXfB2a6mbf0vXG31v8/TUW3wu5XtxSX3z/xvdxY/FvItcLR7JZb66fEY116NAhtG/fvuDrjeuM626Kz4iVfEYU5m95QZt2YZ97X2pw3wNf2ybpFuQzojo+I2YvWhoOeHRig2Vu3GXTJoP1bdu1bVBHrNaxfWiX50wqtaSUdW41vEdzodZdSa37P2rdldS6PiPq8xnhM6KWPiPyrXXVuatS07bsvVjomjaX93ZK27ZtQsd0x1mT9a5osvlHc1PN5nL8tqlx/3P3LcLqndo3+f9VPCax9z0vrnJMonvblcdaW3KcNU5H23jd9+w9PFl3S9Zbycdvm3pN0v1+yu347Uez5+b8N5F6j3fp1LHk/1/lwn7vSmra/7Hfu5KatnZqWnXpqvyfs5JcQfXUpSlyBf+jLl1J9mgl2aPy+IxId4xj7R5dm1xvU/vcze23frZk6SqzjMfjIo07V5f7sdZqqU2rvj1u9+7dV+kgl4uZM2c2uN2jR48Wj6nxOnIdU+Pl042ppdtp/LM3fi0rXbrisin7b7xu2HfDfk124iik2LE63YHrdKHglkp3ELxc15vuJENLxf800v3H0RLxP6R0B2lbIv6nnMt7uNTrjTtGxVhv3JErxnqjSluvz4iVfEYU5m954aKmLwrxGVE9nxEL2676O+6zWveSTSdE4VX6e7RQ1LorqXVX8v/Y//iM8BlRn88InxHV9hmh1q0elf5eLHRNW/D3dpH2/xofm2lq3J27dA5d0mx/QZpjEsX8/yrdWGrp+G22v59yOn4bx9lY4xPQ6cLVcdrnpprcVPJnRKWs1/HbyvyMcI7nf9S0tUldupL/c1ZyHGUl/+f4jGjMZ4TPiFr4jEh3jCPdRcFNae5YTuc0x0UaU5e2jqoPVw8cOLDB7Y8++iin53/88ccNbheic3Pv3r2ToPK8efPqrrSMV3Rle0Ch8c+Qbkzrrbdexp8lk3iVR/1wdQxq9+rVK9SyGHoWOAKgVsWpc1t6oVBjS5evyPnCpVzWDwA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+      "text/plain": [
+       "<Figure size 2880x1148.7 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sbc.compute_rank_statistics(lambda _, param_value: param_value)\n",
+    "plot_ecdf_pit(sbc.simulations, group=\"posterior_sbc\", visuals={\"xlabel\": False})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "- Säilynoja, T., Schmitt, M., Bürkner, P.-C., & Vehtari, A. (2025).\n",
+    "  *Posterior SBC: Simulation-Based Calibration Checking Conditional on Data*.\n",
+    "  [arXiv:2502.03279](https://arxiv.org/abs/2502.03279)\n",
+    "- Talts, S., Betancourt, M., Simpson, D., Vehtari, A., & Gelman, A. (2020).\n",
+    "  *Validating Bayesian Inference Algorithms with Simulation-Based Calibration*.\n",
+    "  [arXiv:1804.06788](https://arxiv.org/abs/1804.06788)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "simuk_dev",
+   "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": "3.14.4"
+  },
+  "tags": [
+   "skip-execution"
+  ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/examples/gallery/prior_sbc.ipynb b/docs/examples/gallery/prior_sbc.ipynb
new file mode 100644
index 0000000..bb5a924
--- /dev/null
+++ b/docs/examples/gallery/prior_sbc.ipynb
@@ -0,0 +1,416 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prior Simulation based calibration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from arviz_plots import plot_ecdf_pit, style\n",
+    "import numpy as np\n",
+    "import simuk\n",
+    "style.use(\"arviz-variat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Out-of-the-box Prior SBC\n",
+    "This example demonstrates how to use the `SBC` class for prior simulation-based calibration, supporting PyMC, Bambi and Numpyro models. By default, the generative model implied by the probabilistic model is used.\n",
+    "\n",
+    "We perform Prior SBC on the centered eight school model, which is known to have a funnel-shaped posterior distribution. The inference algorithm struggles with this model when $\\tau$ is small, and thus we expect to see deviations from the uniform distribution in the rank statistics.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PyMC\n",
+    "\n",
+    "First, define a PyMC model. In this example, we will use the centered eight schools model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymc as pm\n",
+    "\n",
+    "data = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])\n",
+    "sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])\n",
+    "\n",
+    "with pm.Model() as centered_eight:\n",
+    "    mu = pm.Normal('mu', mu=0, sigma=5)\n",
+    "    tau = pm.HalfCauchy('tau', beta=5)\n",
+    "    theta = pm.Normal('theta', mu=mu, sigma=tau, shape=8)\n",
+    "    y_obs = pm.Normal('y', mu=theta, sigma=sigma, observed=data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pass the model to the SBC class, set the number of simulations to 100, and run the simulations. This process may take\n",
+    "some time since the model runs multiple times (100 in this example)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sbc = simuk.SBC(centered_eight,\n",
+    "    num_simulations=100,\n",
+    "    sample_kwargs={'draws': 100, 'tune': 100})\n",
+    "\n",
+    "sbc.run_simulations();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To compare the prior and posterior distributions, we will plot the results from the simulations,\n",
+    "using the ArviZ function `plot_ecdf_pit`.\n",
+    "If the inference algorithm was well-calibrated, we expect a uniform distribution that lies within the 94% credible interval, indicated by not having any red points.\n",
+    "In our case, we see a clear deviation from the uniform distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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VxVWhn/zkJ2G77bbLtZctW5ac88YYqyony3FVOGvf/vvvHzp06FDRYxqvsklelY1r33nKtjTGVLHkv+mV5bgqJP9NjyzHlfyXYsmrAAAAAAAoN8XVNajwhkOpN9YqUWBceLNvyZIlFenTqt5ULFy/a9euJW1frdIYU8V44oknwi9/+cu8ouaxY8eGH//4xxU/9kEHHRR69OiRa8+bNy9MnTq14sfNkqzGVWOOPvrovPZzzz3X6LrGqsrJalzF34v33Xdf3rLRo0e3yrGNV9kjr8rGte88ZVsaY6oY8t90y2pcNUb+mw5ZjSv5L6WQVwEAAAAAUG6Kq2tQt27d8tqffvppSdvHItD6unfvvsp9KtxHqX0qXL+xPq3qcQpfe+F7WavSGFPNmThxYjjppJPC0qVLc8sOOeSQZNbq1hBnmx0yZEjesmnTprXKsbMii3HVlF133TWvPWPGjEbXNVZVTlbj6j//+U+YP39+rj1gwIAwcODAVjm28Sp75FXZuPadp2xLY0w1R/6bflmMq6bIf9Mhq3El/6UU8ioAAAAAAMpNcXUN2mijjfLa77//fknbf/DBB3ntfv36rXKfevXqlXfDb+7cuSXNXl34Ghrr0//93/81+Vqam62p/k3FeONmrbXWKnr7apbGmGrK66+/Ho4//vi8xxt/73vfC7/73e9Ca9pggw2avGld67IWV81Zc801k686X3/9dfj8888bXNdYVTlZjasJEybktUeNGhVak/EqW+RV2bj2nadsS2NMNUX+mw1Zi6vmyH/TIatxJf+lFPIqAAAAAADKTXF1DerRo0dy06HOJ598EhYtWlT09rNnz85rb7rppmXp1yabbJL7fvny5Ssdpxx9qn+MaNasWWU/Ri1Ka0w15M033wzHHnts+OKLL3LLhg0bFsaPHx/atm3dIbFTp06r9HjmapeluCpWx44d89r1C/zrM1ZVThbjKn7w4qmnnsq127VrF0aOHBlak/Eqe+RV2bj2nafsSmtMNUT+mx1ZiqtiyX9XvyzGlfyXlpBXAQAAAABQToqra9Tmm2+e+37ZsmXh1VdfLXrbl19+udF9rYr+/fvntSdPnlz2PhUeo3C7phT2Z7PNNit621qQxpgq9N5774Vjjjkmb4boHXfcMVx55ZWhQ4cOobUVPo65Z8+erd6HtMtCXBUrfmhk/vz5RZ1zY1VlZS2u7r333mSm8zq77bZb6N27d2hNxqvskVdl49p3nrItjTFVSP6bPVmIq2LJf9Mja3El/6Ul5FUAAAAAAJST4uoaNXTo0Lz2xIkTi34c7Jw5c/JmhVl//fXL0qddd921RX2Ks/3WvzHYtWvXMHjw4AbX3XbbbUOXLl1y7VdeeSUsWbKkqOMU9icWt5HumKpv7ty54eijj857BPLAgQPDn//855VmZG0tMf7q69Onz2rpR5qlPa5KMXXq1LwC2VhYvcYaazS4rrGqsrIWV3feeWdee9SoUaG1Ga+yR16VjWvfecq2NMZUffLfbEp7XJVC/pseWYsr+S8tIa8CAAAAAKCcFFfXqL322iuvfc899xS13d13393kflbFHnvsEdq3b59rP/zww0nhdHMeffTRsHDhwlx72LBhjRYsxuX1i6LjdnH75sR+xP7UibMc77777s1uV0vSGFN1vvjii3DssceGt99+O2/m8b/+9a+hW7duYXWYOXNmeOONN3Ltdu3ahe2333619CXN0hxXpbr//vvz2kOGDGl0XWNVZWUprqZNmxZee+21XHvNNdcMe++9d2hNxqtskldl49p3nrItjTFVR/6bXWmOq1LJf9MjS3El/6Wl5FUAAAAAAJST4uoatcUWW4QBAwbkFU498cQTTW7z1VdfhVtvvTVv2f7771+2PvXo0SMpjK7z2Wefhdtvv73ZxwzfcMMNecsOOOCAJrcp7HPcPu6nKbfddlv4/PPP827YdO/evcltak0aY6ruGMcdd1yYMmVKbtmGG24Y/va3v4VevXqF1SHG2/jx4/PiLs5UHK8BshFXpZo9e3a46aab8pbFcaQpxqrKyVJcTZgwIa89YsSIRj9AVAnGq+ySV2Xj2neesi2NMVV3DPlvdqU1rkol/02XLMWV/JeWklcBAAAAAFBOiqtr2IknnpjXPu+885KC5sZccskleY+DjTNnfutb32p0/TvuuCO5gVf39cMf/rDZPo0bNy6vfemll+Yds1AsjH755Zdz7dif4cOHN3mM73znO0l/6kyaNCn84x//aPKmcOxHnTZt2qz03pHOmPr666/Dz372s/DSSy/llvXp0yf8/e9/D+uuu25ZTluMvxhDxVq2bFm46KKLwuOPP563/Pjjjy9Lf6pR2uIqzt42b968ovv/4YcfhhNOOCEsWLAgtyw+SvvAAw9scjtjVW3FVUO++eablWYVHD16dGgp41W2yauq99qX/2Zb2mJK/lsd0hZX8t/qkLa4aoj8l1WNKXkVAAAAAADlori6hsXCve222y7XnjVrVjjiiCPC1KlTV3qkdLzpVr8AuWPHjuGUU04pe58GDRoU9ttvv1w7zhZ92GGHhYkTJ+att3jx4nD11VeHCy+8MK/o+cwzz0z+bUr8+RlnnJG33gUXXBCuueaaZL/1vfjii+Hwww9P3oP6M2NvtdVWq/Q6q1XaYuqss84KTz75ZK7dqVOncP755yfnPhbNF/tVf9byQnHGrx/84AfhqKOOSm78NVZ0G2d/jfEU14uzZte3zz77hN13372Mr7y6pC2u/vWvfyUf4jjnnHPC888/H5YsWdLgeosWLQo333xzUgwbH21dJ8bfr371q2ZnHzZW1VZcNeSZZ54JH3/8ca69ySabJLPct5TxqvKK/T3y6aefNrhe/fNdDvKqbFz7zlO2pS2m5L/VIW1xJf+tDmmLq4bIf7NH/ruCvysCAAAAAFSfNstjxR81K86oevDBB4ePPvoor6Bv4MCBoV+/fmH+/Plh8uTJeTOuRuPHjw8jR45sct+x0PTss8/OtXfaaadw4403NtuneKwxY8bkFSNG8RG2sbBs4cKF4dVXX00Kk+qLN/ri7LDFuvLKK8MVV1yRt6xXr15h6623Dp07dw5vvvlmmD59et7PY1H1LbfcErp06VL0cWpNmmKq/gzlqzrDV5wBu5g+RRtssEHYeOONQ/fu3UP79u2T1/z66683WHg9ePDgZAb2GHNkI67ibFkvvPBCrt2hQ4fQv3//ZFb0bt26JTNGxv6+9tpryfeFfvGLX4Rjjjmm6NNtrKqNuGrIaaedFu6777689nHHHVfSPprqU2S8Kq9V/b3TVJzIq6r72pf/ZluaYkr+Wz3SFFfy3+qRprhqiPw3e+S//q4IAAAAAFCt2q/uDrB6rbvuuuG6664LJ510UnjrrbeSZbHePhYvx69CcbaiOBtaczfVVkXXrl3DtddemxRLT5o0Kbc8FlsXFlxHbdu2TYrNSimsrntUaCx8jMdatmxZsiwWv9af6bi+OMPTZZddprA6gzHV2uKjk+s/Prkxhx56aHLzOb4HZDeu4jgSi+fjV1N69uwZfv/73yeP0y6Fsao24yrOGPjYY4/l/a6rxHGNV9VPXpWNa995yrY0xlRr8/uktuJK/ptdaY4r+S/lIq8CAAAAAKAc2pZlL2RanBF6woQJYezYsWHttdducJ04M+uee+6ZPA74sMMOq3if1ltvvXDzzTeH008/PZlVsyFxdqVddtkleVRtSx5PG7c/9dRTk1mDd95556TdkHj82I/Yn3gjkmzGVKXssMMO4Uc/+lEyq3mcpbo5cWbj0aNHh7vuuiuce+65CqszGFdHHnlk2G+//YoeD+Is5meccUZ45JFHSi6sjoxVtRFXhR544IHw1Vdf5drx91383bgqjFe1S16VjWvfecq2NMZUpfh9UntxJf+tLmmJq0LyX8pJXgUAAAAAwKpqszxOUQP/39KlS8N///vfMHv27PDJJ58ks7307ds3mbW5V69eq+V9iiH68ssvh3feeSd5dG2nTp2SosZBgwatcqFZfe+9914yU1N8TG4saOvTp0/YaKONwuDBgxstvCabMVUpixcvDtOnT09m7vv444/DwoULwzfffBO6d+8eevToEfr375/cyI4z0FIdcTV37twwY8aM8P7774dPP/00GTtikX083+uss04yTvXu3busxzRWVX9ctQbjVe2SV2Xj2neesi2NMVUpfp/UXlzJf6tLWuKqNRivape8CgAAAACAllBcDQAAAAAAAAAAAAAQQjB9KgAAAAAAAAAAAACA4moAAAAAAAAAAAAAgBXMXA0AAAAAAAAAAAAAoLgaAAAAAAAAAAAAAGAFM1cDAAAAAAAAAAAAACiuBgAAAAAAAAAAAABYwczVAAAAAAAAAAAAAACKqwEAAAAAAAAAAAAAVjBzNQAAAAAAAAAAAACA4moAAAAAAAAAAAAAgBXMXA0AAAAAAAAAAAAAoLgaAAAAAAAAAAAAAGAFM1cDAAAAAAAAAAAAACiuBgAAAAAAAAAAAABYwczVAAAAAAAAAAAAAACKqwEAAAAAAAAAAAAAVjBzNQAAAAAAAAAAAACA4moAAAAAAAAAAAAAgBXMXA0AAAAAAAAAAAAAoLgaAAAAAAAAAAAAAGAFM1cDAAAAAAAAAAAAACiuBgAAAAAAAAAAAABYwczVAAAAAAAAAAAAAACKqwEAAAAAAAAAAAAAVjBzNQAAAAAAAAAAAACA4moAAAAAAAAAAAAAgBXMXA0AAAAAAAAAAAAABEL4f3HfexHtXEuzAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 2880x1148.7 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_ecdf_pit(sbc.simulations,\n",
+    "              visuals={\"xlabel\":False},\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bambi\n",
+    "\n",
+    "Now, we define a Bambi Model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import bambi as bmb\n",
+    "import pandas as pd\n",
+    "\n",
+    "x = np.random.normal(0, 1, 200)\n",
+    "y = 2 + np.random.normal(x, 1)\n",
+    "df = pd.DataFrame({\"x\": x, \"y\": y})\n",
+    "bmb_model = bmb.Model(\"y ~ x\", df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pass the model to the `SBC` class, set the number of simulations to 100, and run the simulations.\n",
+    "This process may take some time, as the model runs multiple times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sbc = simuk.SBC(bmb_model,\n",
+    "    num_simulations=100,\n",
+    "    sample_kwargs={'draws': 25, 'tune': 50})\n",
+    "\n",
+    "sbc.run_simulations();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To compare the prior and posterior distributions, we will plot the results from the simulations.\n",
+    "If the inference algorithm was well-calibrated, we expect a uniform distribution that lies within the 94% credible interval, indicated by not having any red points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<arviz_plots.plot_collection.PlotCollection at 0x206de31cb90>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3600x535.887 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_ecdf_pit(sbc.simulations)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Numpyro\n",
+    "\n",
+    "We define a Numpyro Model, we use the centered eight schools model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpyro\n",
+    "import numpyro.distributions as dist\n",
+    "from jax import random\n",
+    "from numpyro.infer import NUTS\n",
+    "\n",
+    "y = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])\n",
+    "sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])\n",
+    "\n",
+    "def eight_schools_cauchy_prior(J, sigma, y=None):\n",
+    "    mu = numpyro.sample(\"mu\", dist.Normal(0, 5))\n",
+    "    tau = numpyro.sample(\"tau\", dist.HalfCauchy(5))\n",
+    "    with numpyro.plate(\"J\", J):\n",
+    "        theta = numpyro.sample(\"theta\", dist.Normal(mu, tau))\n",
+    "    numpyro.sample(\"y\", dist.Normal(theta, sigma), obs=y)\n",
+    "\n",
+    "# We use the NUTS sampler\n",
+    "nuts_kernel = NUTS(eight_schools_cauchy_prior)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pass the model to the `SBC` class, set the number of simulations to 100, and run the simulations. For numpyro model,\n",
+    "we pass in the ``data_dir`` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [01:49<00:00,  1.09s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "sbc = simuk.SBC(nuts_kernel,\n",
+    "    sample_kwargs={\"num_warmup\": 50, \"num_samples\": 75},\n",
+    "    num_simulations=100,\n",
+    "    data_dir={\"J\": 8, \"sigma\": sigma, \"y\": y},\n",
+    ")\n",
+    "sbc.run_simulations()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To compare the prior and posterior distributions, we will plot the results from the simulations,\n",
+    "using the ArviZ function `plot_ecdf_pit`.\n",
+    "If the inference algorithm was well-calibrated, we expect a uniform distribution that lies within the 94% credible interval, indicated by not having any red points.\n",
+    "In our case, we see a clear deviation from the uniform distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RSzONMQNAO3/vWcnaiHbLRs2uUvtbvbZbtWpxktZjJCETA7QuxdU0peunvV50oVwMVCMLhKByBgGTtF9uf9KFhDEQZpMr5BXbTjVfm2o+dzPK9ZrlOpiN6xeznxW7zzXyQXSxf2ON1H6li9gyVWowJNtjkp6wUemDzaTtV3sQqVlOaAGoVjYuR7Xbh2ZUzt9FvuODJOu3Yh5xUm9z8XoBabe+PCuc+Z9nU79//V0bhA+OHV1yps3HGCRU//uLXJKOTdYjo8ok9WX7AzTW+Gw7nyDVbuPXlfr/1mu7VSPLVqMeI0n75WrFcV6ARqa4mqYcfGnXGUjj/zn+32ORYKXaqWTwSvLaVOu5W029Dmbb+SC62mp5sFmp1zHpCRuZ61fi/1iN9lv1hBaAds7G0E6KzXetnkec1NtcvF5A2rLOznBORnaNv79/rVGhszNUPNNWahwVqF52rXVGlUnqy/YHABpJtesiqtV+q43zAjS6jnp3AIoVZx5NMtAeQ0U8ayuXeF9cp9x2KiVpf+L/Pd/st5Vqp5qvTTWeG6DS0l/ExsLFRmgHoJrZGGhtrZRHijmptxX+v83O6wVkmrt4aZidMXtX/D3eljTTFssYJDS2WmY2maS+bH+A+mu02gigNMY9AWpLcTUtKX22Vr6ZkeN9cZ18BxFJ2qmUJP2pZTu0/sGsg+jWkPR1LFZ6f6v2ftLo+38jn9ACkFQtMy1QvmLzUbvlESf1NhevFwC0tnLHJmuVUWWS+rL9Aeqv0WojoJYa5fvuSmmVcV6AZuCUM1rC5IkTwsoD+/cIL0lCf7xURrw0ZK7gkbSdSsnWnzmLlhR9yZBKtVOp1yaqx3M328FsvtnXqnEwW6/npbKSvI7F6v26V3M/aeT9v5btADRCNgYaQyn5SB4BoNUzbRL1Gv+EdmZsDACaR6PVRkCtVPv7aJkYoHW1dXH10qVLwwMPPBCmT58eZs6cGYYNGxbGjBkTtt566zBq1Ki69KmzszM8/PDD4cUXXwwzZswIgwcPTvVp/PjxYY011qhLn5pBHGgfWeRge2bQKfWx1ZCkP/GLgmq0k+2AKV6uLelZb9n6letLkCTP3ex6b7t8r1u9DmYdRLeGQq9jsXrvb9XeTxpp/2/2E1qA1pckX5STjYHGUGw+SppHWvU4zEm9zcXrBWTKNV5WqUybZBwVqN3YZJKMmksx2TXJsbNMUl+2P9AO3/82okarjYBaqff33eXwvTNA/bRlcfWCBQvCz372szBlypTwxhtvrHB///79wy677BK+/OUvh0022aRmhd4XXXRRmDx5cqrYu7eOjo6www47hGOOOSZst912oV3c+vKscOZ/nk39/vV3bRA+OHZ0vbvUECpVsNe7nfTZeDH4RddPe71qs7EWeu5mV8q2q9fBrIPo1lDt17HZ22+WE1oA8mXgamYzoPEUm4+SrN+qx2HVOKk334nG1cxxxZzgXIv+VOskoGzkZWhP1T4x2YnPUBvljO0l/TtNml2THjsnzSTNkMGaUTmZsNkzM9D4jM9Ca2ql77uNowHURtsVVz/99NPhuOOOC88991zOdZYsWRJuvfXWcOedd4ZTTjklfOpTn6pqn1577bVUIfeDDz6Yc53ly5eHu+++O9xzzz3hqKOOSq3f6pZ1doZzMgbA4u/vX6s+M4q3i7it46BjPKMuqmXxTuZzN/sAVxzYU/gEraNWJ7QA5MvAnZ21zWZAe2il47BK5q9CBTnVynGlnkTTKLmy3JOA5GUAoNzsWomx+UoXe9MYkwJ5vYCkjM8CzcA4GkBtdIQ2MnPmzPCFL3xhhcLq8ePHhw996EPhPe95Txg6dGj37YsWLQrf/OY3w9VXX121Pr399tvh8MMPX6GweqONNgp77rln2GmnncLKK6/co8g6zrp94YUXhlY3d/HSMDvjDPX4e7yt3cSz6eOgTyFxnbhuue3EAal4xn/8Kbd4J92nYp+72SXddoVeM6A9vhCKX/oAFMrA8gVQrHY7DqtU/kpSkFONHFdOIVAj5MpqnGTcCP8voDHHOevVPlC+pH+npWbXWh47yyq1kbmdmz0zA83B+CzQjGQdgOpom+Lqzs7O1IzVM2bM6L5t4403Dn/5y1/ClClTwrnnnhsuueSS8Pe//z18+tOf7vHYU089NTXjdTWcdtpp4amnnupeHjNmTPjd734Xrr322nDeeeeF3/zmN+H2229PzVTdp89/z8T/yU9+Ev75z39WpU80ljgDQzwrP9+AY/qM+3wzjSVpp5Iy+1Tr524GSV4zoH1OaAGoBPkCyNRux2GVyl9JC3IqnePKPcG53rmy2EImeRnaU6XGOevVPtAaGTXzfaDaxd7kV+tJgbxeQLXJmkA1GEcDqJ+2mZ7hpptuCg888ED38tixY1NFzCNGjOix3vDhw1MFz7GQ+bLLLuuewToWX59//vkV7dOjjz4a/vrXv/Z47ssvvzzVt0yDBg0KRx99dBgyZEg466yzuovFf/CDH4Q//elPPYquW92cjFn82km83Fm81F2uQboYppJ8IZCtnbhNe18yJNd2njxxQlh5YP9Efe7dp6TP3ap6b7ukrxlQny94klwGPskJLZWevQ9oT0mzmXwB9NZOx2HyV+PrnaPlZWhPlRrnrFf7QPkK/Z1mqkR2zXfsLEPWl+0PNAvjs0A9yUwA9dM2xdW9C6NPP/30FQqrM331q18Nf/vb38L06dNTyzfffHOYOnVq2GyzzarWp+OPP36FwupMhx56aLjuuuvCQw91DSQ99thj4dZbbw277757aBet+AVwMYFpZMLC5nLbybWd4wBkOX1I8tzlFtBX4guSeFm4Yr6A6b1+tv9DudsOaN0TWgAKqVY2A9pDKcdhzVp4Vu4JxbnuiwU5UaF2itluSY4j853gXOz/qxylHgcXOglIXob2Valxznq1D9T377TYLFfo2LncYu9GztLFftdRD9WcFKjZXi+gcRmfBVph3LNS44ClttOKWRZofW1RXP3kk0+Gp556qnt5/fXXD7vuumvexwwePDhMmjQp/PCHP+y+7ZprrqlYcfVbb70V/vGPf/SYtfrjH/943sfEGapjgfUJJ5zQo0/tVFxN6yu36DA9E1YMl6W4ftrriWasTbdfaH2gOdXyhJakB7kOEpufQQAAmuU4rNzjqnoq54TiXHIVa5S63ZIeRxZ7Ek21TuIr9Tg4Sf/bqfgfAKiMamSecsYCGzVLF/tdRz3VclKgRn29AADqMe5ZrXqYWtftAFRLWxRX33bbbT2W99lnn0SP++hHP9qjuDrOZH3SSSdVpE933HFHWLr0v2fY7LnnnmHgwIEFHxcLqWPh94IFC1LLd955Z1i8eHEYMGBAaDXxy7L4gVjoQzquE9fFdo7i/hJDVjxrr9gvW2PRW6FgmNl+pLAaKFfSg1wHic3NIABJycBAsx9XtbMk2y3JcWejqfdxsAIYAKCZNEKWLva7jnbO/LYD0JvxWaCdVWocsJZ1O+2cZYHq6wht4K677uqxvO222yZ63BprrBHWWmut7uXnn38+vPLKKxXp0z//+c+S+hQLsLfccsvu5fnz54eHH344tKL4ARjPNIrFZIUKzXxYNud2Th+cVloMUsVeliSKj0kSDNPtJ13fCQBAJaQPEuMBJc2lmEEAry8yMFBtSY/DSj2uajTlHnemj+cqtd0qdRxZrePpZjgOlpsAoH1UKsu1W5Yu9ruORpN0O1cqMzfqdgDqw/gs0CyqXW+TNFMWaqfdsizQWtpiut9nnnmm+/eOjo6wxRZbJH7shAkTwvTp03u0teaaa5bdp6effrrHcmbBdJI+3XvvvT3aSlqc3WziJRzimUa5PhBdCra5t3P64LTZZu0qhhMAgFJmPCh0kFjqZUqpj2IHAby+yMBANbXDcVil/r+9j+dqtd2SHEc28utYznFw0rwsNwFAe6hklqt3f6jsdm72zAw0NuOzQDOQdQCqr+WLq996663w5ptvdi+PHj06DB48OPHjx44d22M5zl69yy67lN2v2E5anz59wrhx48rqU6sHAkVGrbudCx2cJjFn0ZIVLhMcb6tEgfjkiRNS/xZqP3P9lTO2oxMAgEwOcoGkZGCg1sdh2Y6rWkWpx529j+eqtd1KPY6sxPF0LsX8vyp5HCwvAwDVynLtnqVzfdfR6oWNzfp6AfVnfBZoBtWqt0k6DlhqO62eZYHW0fLF1dOmTeuxvMYaaxT1+DFjxuRtrxSx2Hv+/Pndy6NGjQoDBgyoa58azYIFC0p+bP/+/UO/fv0q3m5sM7adzcKFC0NnZ2dJ7fbt2zfn679o0aKwfPnyktqNs7QPHDiwpu0uXrw4LFtW2tn/g/v0CYMGDcp635IlS8LSpbnD4KDlKz5n71CVnsUghsu02GZsO23hoqWJ2s7WfloMksP7daS2Rarvi5aG7GXYucXtEE+66C1u23S7pYivW3z9eov7QtwnShX337gf9xb/JuLfRqXbjbxHtN97RJ8y3iMKyXXSVe/3iEq1u8dao8JOo4cmmi3lrSVLw6F3PNHjttkLs588kvkesXR5Z9GzsYweNiQM6Nc30XtEvvbj+23mFxveI7J/vuQaBJgxd35YmHE5097bM1+OiK9L5uDJ4tTfclc+WW3EsNA3y2cLjcXnWxefb835+VZuVpWBa5+B018SptvNduxT6HOpHsfJ8fNthedasDAsWF7472VIkRk41/FcZirNtt0yT8aNfxfLly3vke9WaG/5sjBoeZ8VnjdpBs6+RnnvEbn2h2wy+1+J94j3rzos7PTBLXrkzQV9+oZP3bLiSc+x3SWLlyTKTdk4Tm7usbTe+TczA8u/jUfW7SLrdpF1uxjvLe49YlADfY4tXbwoDMr4HCsmS5fznVC+MbnhA/uHoYNzJ8Nc33UUyvyljPf27me2DLwofneQozAnSdbN991L70yaPvYplHUzt0M2gwYPylrU7Xi4i++EKn88bLy39mTWLjJrF5m1i8zaHO8R2SZSTPp5lC0bvT7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+      "text/plain": [
+       "<Figure size 2880x1148.7 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_ecdf_pit(sbc.simulations,\n",
+    "              visuals={\"xlabel\":False},\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Custom simulator SBC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PyMC\n",
+    "\n",
+    "In certain scenarios, you might want to pass a custom function to the `SBC` class to generate the data. For instance, if you aim to evaluate the effect of model misspecification by generating data from a different model than the one used for model fitting.\n",
+    "\n",
+    "Next, we determine the impact of occasional large deviations (outliers) by drawing from a Laplace distribution instead of a normal distribution (which we use to fit the model)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(theta, seed, **kwargs):\n",
+    "    rng = np.random.default_rng(seed)\n",
+    "    # Here we use a Laplace distribution, but it could also be some mechanistic simulator\n",
+    "    scale = sigma / np.sqrt(2)\n",
+    "    return {\"y\": rng.laplace(theta, scale)}\n",
+    "\n",
+    "sbc = simuk.SBC(centered_eight,\n",
+    "    num_simulations=100,\n",
+    "    simulator=simulator,\n",
+    "    sample_kwargs={'draws': 25, 'tune': 50})\n",
+    "\n",
+    "sbc.run_simulations();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bambi\n",
+    "\n",
+    "In certain scenarios, you might want to pass a custom function to the `SBC` class to generate the data. For instance, if you aim to evaluate the effect of model misspecification by generating data from a different model than the one used for model fitting.\n",
+    "\n",
+    "Next, we determine the impact of occasional large deviations (outliers) by drawing from a Laplace distribution instead of a normal distribution (which we use to fit the model)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(mu, seed, sigma, **kwargs):\n",
+    "    rng = np.random.default_rng(seed)\n",
+    "    # Here we use a Laplace distribution, but it could also be some mechanistic simulator\n",
+    "    scale = sigma / np.sqrt(2)\n",
+    "    return {\"y\": rng.laplace(mu, scale)}\n",
+    "\n",
+    "sbc = simuk.SBC(bmb_model,\n",
+    "    num_simulations=100,\n",
+    "    simulator=simulator,\n",
+    "    sample_kwargs={'draws': 25, 'tune': 50})\n",
+    "\n",
+    "sbc.run_simulations();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Numpyro\n",
+    "\n",
+    "In certain scenarios, you might want to pass a custom function to the `SBC` class to generate the data. For instance, if you aim to evaluate the effect of model misspecification by generating data from a different model than the one used for model fitting.\n",
+    "\n",
+    "Next, we determine the impact of occasional large deviations (outliers) by drawing from a Laplace distribution instead of a normal distribution (which we use to fit the model)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(theta, seed, **kwargs):\n",
+    "    rng = np.random.default_rng(seed)\n",
+    "    # Here we use a Laplace distribution, but it could also be some mechanistic simulator\n",
+    "    scale = sigma / np.sqrt(2)\n",
+    "    return {\"y\": rng.laplace(theta, scale)}\n",
+    "\n",
+    "sbc = simuk.SBC(nuts_kernel,\n",
+    "    sample_kwargs={\"num_warmup\": 50, \"num_samples\": 75},\n",
+    "    num_simulations=100,\n",
+    "    simulator=simulator,\n",
+    "    data_dir={\"J\": 8, \"sigma\": sigma, \"y\": y}\n",
+    ")\n",
+    "\n",
+    "sbc.run_simulations();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "simuk_dev",
+   "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": "3.14.4",
+  "tags": ["skip-execution"]
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/examples/gallery/sbc.md b/docs/examples/gallery/sbc.md
deleted file mode 100644
index 12bd166..0000000
--- a/docs/examples/gallery/sbc.md
+++ /dev/null
@@ -1,240 +0,0 @@
----
-jupytext:
-  text_representation:
-    extension: .md
-    format_name: myst
-kernelspec:
-  display_name: Python 3
-  language: python
-  name: python3
----
-
-# Simulation based calibration
-
-```{jupyter-execute}
-
-from arviz_plots import plot_ecdf_pit, style
-import numpy as np
-import simuk
-style.use("arviz-variat")
-```
-
-## Out-of-the-box SBC
-This example demonstrates how to use the `SBC` class for simulation-based calibration, supporting PyMC, Bambi and Numpyro models. By default, the generative model implied by the probabilistic model is used.
-
-
-::::::{tab-set}
-:class: full-width
-
-:::::{tab-item} PyMC
-:sync: pymc_default
-
-First, define a PyMC model. In this example, we will use the centered eight schools model.
-
-```{jupyter-execute}
-
-import pymc as pm
-
-data = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])
-sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])
-
-with pm.Model() as centered_eight:
-    mu = pm.Normal('mu', mu=0, sigma=5)
-    tau = pm.HalfCauchy('tau', beta=5)
-    theta = pm.Normal('theta', mu=mu, sigma=tau, shape=8)
-    y_obs = pm.Normal('y', mu=theta, sigma=sigma, observed=data)
-```
-
-Pass the model to the SBC class, set the number of simulations to 100, and run the simulations. This process may take
-some time since the model runs multiple times (100 in this example).
-
-```{jupyter-execute}
-
-sbc = simuk.SBC(centered_eight,
-    num_simulations=100,
-    sample_kwargs={'draws': 25, 'tune': 50})
-
-sbc.run_simulations();
-```
-
-To compare the prior and posterior distributions, we will plot the results from the simulations,
-using the ArviZ function `plot_ecdf_pit`.
-We expect a uniform distribution, the gray envelope corresponds to the 94% credible interval.
-
-```{jupyter-execute}
-
-plot_ecdf_pit(sbc.simulations,
-              visuals={"xlabel":False},
-);
-```
-
-:::::
-
-:::::{tab-item} Bambi
-:sync: bambi_default
-
-Now, we define a Bambi Model.
-
-```{jupyter-execute}
-
-import bambi as bmb
-import pandas as pd
-
-x = np.random.normal(0, 1, 200)
-y = 2 + np.random.normal(x, 1)
-df = pd.DataFrame({"x": x, "y": y})
-bmb_model = bmb.Model("y ~ x", df)
-```
-
-Pass the model to the `SBC` class, set the number of simulations to 100, and run the simulations.
-This process may take some time, as the model runs multiple times
-
-```{jupyter-execute}
-
-sbc = simuk.SBC(bmb_model,
-    num_simulations=100,
-    sample_kwargs={'draws': 25, 'tune': 50})
-
-sbc.run_simulations();
-```
-
-To compare the prior and posterior distributions, we will plot the results from the simulations.
-We expect a uniform distribution, the gray envelope corresponds to the 94% credible interval.
-
-```{jupyter-execute}
-plot_ecdf_pit(sbc.simulations)
-```
-
-:::::
-
-:::::{tab-item} Numpyro
-:sync: numpyro_default
-
-We define a Numpyro Model, we use the centered eight schools model.
-
-```{jupyter-execute}
-import numpyro
-import numpyro.distributions as dist
-from jax import random
-from numpyro.infer import NUTS
-
-y = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])
-sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])
-
-def eight_schools_cauchy_prior(J, sigma, y=None):
-    mu = numpyro.sample("mu", dist.Normal(0, 5))
-    tau = numpyro.sample("tau", dist.HalfCauchy(5))
-    with numpyro.plate("J", J):
-        theta = numpyro.sample("theta", dist.Normal(mu, tau))
-    numpyro.sample("y", dist.Normal(theta, sigma), obs=y)
-
-# We use the NUTS sampler
-nuts_kernel = NUTS(eight_schools_cauchy_prior)
-```
-
-Pass the model to the `SBC` class, set the number of simulations to 100, and run the simulations. For numpyro model,
-we pass in the ``data_dir`` parameter.
-
-```{jupyter-execute}
-sbc = simuk.SBC(nuts_kernel,
-    sample_kwargs={"num_warmup": 50, "num_samples": 75},
-    num_simulations=100,
-    data_dir={"J": 8, "sigma": sigma, "y": y},
-)
-sbc.run_simulations()
-```
-
-To compare the prior and posterior distributions, we will plot the results.
-We expect a uniform distribution, the gray envelope corresponds to the 94% credible interval.
-
-```{jupyter-execute}
-plot_ecdf_pit(sbc.simulations,
-              visuals={"xlabel":False},
-);
-```
-
-:::::
-
-::::::
-
-## Custom simulator SBC
-
-::::::{tab-set}
-:class: full-width
-
-:::::{tab-item} PyMC
-:sync: pymc_custom
-
-In certain scenarios, you might want to pass a custom function to the `SBC` class to generate the data. For instance, if you aim to evaluate the effect of model misspecification by generating data from a different model than the one used for model fitting.
-
-Next, we determine the impact of occasional large deviations (outliers) by drawing from a Laplace distribution instead of a normal distribution (which we use to fit the model).
-
-```{jupyter-execute}
-def simulator(theta, seed, **kwargs):
-    rng = np.random.default_rng(seed)
-    # Here we use a Laplace distribution, but it could also be some mechanistic simulator
-    scale = sigma / np.sqrt(2)
-    return {"y": rng.laplace(theta, scale)}
-
-sbc = simuk.SBC(centered_eight,
-    num_simulations=100,
-    simulator=simulator,
-    sample_kwargs={'draws': 25, 'tune': 50})
-
-sbc.run_simulations();
-```
-
-:::::
-
-:::::{tab-item} Bambi
-:sync: bambi_custom
-
-In certain scenarios, you might want to pass a custom function to the `SBC` class to generate the data. For instance, if you aim to evaluate the effect of model misspecification by generating data from a different model than the one used for model fitting.
-
-Next, we determine the impact of occasional large deviations (outliers) by drawing from a Laplace distribution instead of a normal distribution (which we use to fit the model).
-
-```{jupyter-execute}
-def simulator(mu, seed, sigma, **kwargs):
-    rng = np.random.default_rng(seed)
-    # Here we use a Laplace distribution, but it could also be some mechanistic simulator
-    scale = sigma / np.sqrt(2)
-    return {"y": rng.laplace(mu, scale)}
-
-sbc = simuk.SBC(bmb_model,
-    num_simulations=100,
-    simulator=simulator,
-    sample_kwargs={'draws': 25, 'tune': 50})
-
-sbc.run_simulations();
-```
-
-:::::
-
-
-:::::{tab-item} Numpyro
-:sync: numpyro_custom
-
-In certain scenarios, you might want to pass a custom function to the `SBC` class to generate the data. For instance, if you aim to evaluate the effect of model misspecification by generating data from a different model than the one used for model fitting.
-
-Next, we determine the impact of occasional large deviations (outliers) by drawing from a Laplace distribution instead of a normal distribution (which we use to fit the model).
-
-```{jupyter-execute}
-def simulator(theta, seed, **kwargs):
-    rng = np.random.default_rng(seed)
-    # Here we use a Laplace distribution, but it could also be some mechanistic simulator
-    scale = sigma / np.sqrt(2)
-    return {"y": rng.laplace(theta, scale)}
-
-sbc = simuk.SBC(nuts_kernel,
-    sample_kwargs={"num_warmup": 50, "num_samples": 75},
-    num_simulations=100,
-    simulator=simulator,
-    data_dir={"J": 8, "sigma": sigma, "y": y}
-)
-
-sbc.run_simulations();
-```
-
-:::::
-
-::::::
diff --git a/docs/examples/img/posterior_sbc.png b/docs/examples/img/posterior_sbc.png
new file mode 100644
index 0000000..b6c67fa
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diff --git a/docs/examples/img/prior_sbc.png b/docs/examples/img/prior_sbc.png
new file mode 100644
index 0000000..dfb79e2
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diff --git a/docs/examples/img/sbc.png b/docs/examples/img/sbc.png
deleted file mode 100644
index af495fd..0000000
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diff --git a/docs/index.rst b/docs/index.rst
index 4dadc01..e5b8bc0 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -2,10 +2,15 @@ Overview
 ========
 
 Simuk is a Python library for simulation-based calibration (SBC) and the generation of synthetic data.
-Simulation-Based Calibration (SBC) is a method for validating Bayesian inference by checking whether the
-posterior distributions align with the expected theoretical results derived from the prior.
 
-Quickstart
+Prior Simulation-Based Calibration (Prior SBC) is a method for validating Bayesian inference by checking
+whether the posterior distributions align with the expected theoretical results derived from the prior.
+
+Posterior Simulation-Based Calibration (Posterior SBC) is a method for validating Bayesian inference by
+checking whether the posterior distributions conditioned on the augmented data (original + posterior predictive) 
+align with the expected theoretical results derived from the posterior.
+
+Prior SBC Quickstart
 ----------
 
 This quickstart guide provides a simple example to help you get started. If you're looking for more examples
@@ -52,6 +57,71 @@ Plot the empirical CDF to compare the differences between the prior and posterio
 The lines should be nearly uniform and fall within the oval envelope. It suggests that the prior and posterior distributions
 are properly aligned and that there are no significant biases or issues with the model.
 
+Posterior SBC Quickstart
+------------------------
+
+While Prior SBC checks the global validity of an inference algorithm across the entire prior space, 
+Posterior SBC evaluates validity locally, conditional on your observed data. To use it, simply pass ``method="posterior"`` and the original ``trace`` to the ``SBC`` class:
+Currently, it's only implemented for PyMC.
+
+.. warning::
+
+    **Model requirements for Posterior SBC**
+
+    Posterior SBC augments the observed data (concatenating original + replicated),
+    which changes its size. For this to work, store observed data in ``pm.Data``
+    containers, and specify size using the ``dims`` parameter instead of setting a static shape. 
+    If your model uses ``dims`` and ``coords``, you are also responsible for resizing them to the correct size corresponding to the new augmented dataset via the ``update_data`` callback.
+    Similarly, if your model has covariates, store them in ``pm.Data`` so they
+    can be resized in the same callback.
+
+.. code-block:: python
+
+    # Define the model conforming to the Posterior SBC implementation requirements.
+    import numpy as np
+    import pymc as pm
+
+    data = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])
+    sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])
+
+    with pm.Model(coords={"school": np.arange(8)}) as centered_eight:
+        school_idx = pm.Data("school_idx", np.arange(8))
+        y_data = pm.Data("y_data", data)
+        sigma_data = pm.Data("sigma_data", sigma)
+        
+        mu = pm.Normal('mu', mu=0, sigma=5)
+        tau = pm.HalfCauchy('tau', beta=5)
+        theta = pm.Normal('theta', mu=mu, sigma=tau, dims="school")
+        y_obs = pm.Normal('y', mu=theta[school_idx], sigma=sigma_data, observed=y_data)
+
+    # Run the model and save the trace.
+    with centered_eight:
+        idata = pm.sample(progressbar=False)
+    
+    # Define necessary callbacks to resize our covariates
+    def update_data(model, augmented_data, simulation_idx):
+        with model:
+            pm.set_data({
+                "sigma_data": np.concatenate([sigma, sigma]),
+                "school_idx": np.concatenate([np.arange(8), np.arange(8)])
+            })
+    
+    # Run Posterior SBC
+    post_sbc = simuk.SBC(
+        centered_eight,
+        method="posterior",
+        trace=idata,
+        update_data=update_data,
+        num_simulations=100,
+        sample_kwargs={'draws': 25, 'tune': 50},
+        progress_bar=False
+    )
+    post_sbc.run_simulations()
+
+    plot_ecdf_pit(post_sbc.simulations, group="posterior_sbc", visuals={"xlabel": False})
+
+For more advanced use cases, such as custom data augmentation or re-evaluating rank statistics, check out the :doc:`Posterior SBC tutorial <examples/gallery/posterior_sbc>`.
+
 .. toctree::
    :maxdepth: 1
    :hidden:
diff --git a/ecdf.png b/ecdf.png
deleted file mode 100644
index 48c0deb..0000000
Binary files a/ecdf.png and /dev/null differ
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 93d7627..387a6ec 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -2,9 +2,9 @@ pytest-cov>=2.6.1
 pytest>=4.4.0
 pre-commit>=2.19
 ipytest==0.13.0
-pymc>=5.20.1
-bambi>=0.13.0
-arviz_base>=0.5.0
+pymc>=6.0.0
+bambi>=0.18.0
+arviz_base>=1.0.0
 ruff==0.15.13
 numpyro>=0.17.0
 numba>=0.60.0
diff --git a/simuk/sbc.py b/simuk/sbc.py
index 5394388..e678d38 100644
--- a/simuk/sbc.py
+++ b/simuk/sbc.py
@@ -1,6 +1,20 @@
-"""Simulation based calibration (Talts et. al. 2018) in PyMC."""
+"""Simulation-based calibration checking (SBC) for PyMC, Bambi, and NumPyro.
+
+Implements both Prior SBC (Talts et al., 2020) and Posterior SBC
+(Säilynoja et al., 2025).
+
+References
+----------
+.. [1] Talts, S., Betancourt, M., Simpson, D., Vehtari, A., & Gelman, A. (2020).
+   Validating Bayesian Inference Algorithms with Simulation-Based Calibration.
+   arXiv:1804.06788.
+.. [2] Säilynoja, T., Schmitt, M., Bürkner, P.-C., & Vehtari, A. (2025).
+   Posterior SBC: Simulation-Based Calibration Checking Conditional on Data.
+   arXiv:2502.03279.
+"""
 
 import logging
+import traceback
 from copy import copy
 from importlib.metadata import version
 
@@ -45,61 +59,161 @@ def wrapped(cls, *args, **kwargs):
 
 
 class SBC:
-    """Set up class for doing SBC.
+    r"""Simulation-based calibration checking (SBC).
+
+    Supports two modes of operation:
+
+    - **Prior SBC** (``method="prior"``, default): validates that the inference
+      algorithm across the prior. Reference draws come from the prior and replicated data
+      from the prior predictive (Talts et al., 2020 [1]_).
+    - **Posterior SBC** (``method="posterior"``): validates that the inference
+      algorithm across the posterior. Reference draws come from the original posterior
+      and replicated data from the posterior predictive. The model is then re-fit on the
+      concatenation of the original observations and the replicated data
+      (Säilynoja et al., 2025 [2]_).
 
     Parameters
     ----------
     model : pymc.Model, bambi.Model or numpyro.infer.mcmc.MCMCKernel
-        A PyMC, Bambi model or Numpyro MCMC kernel. If a PyMC model the data needs to be defined as
-        mutable data.
-    num_simulations : int
-        How many simulations to run
-    sample_kwargs : dict[str] -> Any
-        Arguments passed to pymc.sample or bambi.Model.fit
-    seed : int (optional)
+        A PyMC, Bambi model or NumPyro MCMC kernel. If a PyMC model the
+        data needs to be defined as mutable data.
+    method : {"prior", "posterior"}, default "prior"
+        Which variant of SBC to perform.
+    num_simulations : int, default 1000
+        How many SBC iterations to run.
+    sample_kwargs : dict, optional
+        Keyword arguments forwarded to ``pymc.sample`` (or
+        ``bambi.Model.fit`` / ``numpyro.infer.MCMC``).
+    seed : int, optional
         Random seed. This persists even if running the simulations is
         paused for whatever reason.
-    data_dir : dict
+    data_dir : dict, optional
         Keyword arguments passed to numpyro model, intended for use when providing
         an MCMC Kernel model.
-    simulator : callable
-        A custom simulator function that takes as input the model parameters and
-        a int parameter named `seed`, and must return a dictionary of named observations.
+    simulator : callable, optional
+        A custom data-generating function. It receives the model
+        parameter values as keyword arguments plus a ``seed`` integer,
+        and must return a ``dict`` mapping observed-variable names to
+        numpy arrays.
+    trace : arviz.InferenceData, optional
+        Required for ``method="posterior"``. An InferenceData object that
+        contains both the ``posterior`` and ``observed_data`` groups.
+        The number of posterior draws per chain must be at least ``num_simulations``.
+    augment_observed : callable, optional
+        *Posterior SBC only.* Signature:
+        ``(model, observed_data, replicated_data, simulation_idx) -> dict``.
+        Builds the augmented observed data that the model will be
+        conditioned on. ``observed_data`` is the xarray Dataset from
+        ``trace["observed_data"]``, and ``replicated_data`` is a
+        ``dict[str, np.ndarray]`` of the simulated observations from the
+        original posterior predictive for the current iteration.
+        The returned ``dict`` maps variable names to the augmented data.
+
+        The **default** behaviour concatenates the original and replicated
+        observations along the first axis for each variable. Provide
+        this callback when simple concatenation is not valid, e.g. for
+        structured data.
+    update_data : callable, optional
+        *Posterior SBC only.* Signature:
+        ``(model, augmented_data, simulation_idx) -> None``.
+        Called *before* conditioning the model on the augmented data.
+        Use this to resize covariates, coordinate labels, or other
+        ``pm.Data`` containers so that the model is consistent with the
+        augmented dataset.
     transform : callable, optional
         A transform applied to both the reference draw and the posterior
         draws before computing the rank statistic. Signature:
-        ``(param_name, param_value) -> transformed_value``. Useful for defining scalar
-        test quantities (e.g. ``lambda param_name, param_value: np.mean(param_value)``
-        to test the mean of a vector parameter). The return values must be comparable
-        with the ``<`` operator. The default is the identity (rank on the raw parameter values).
+        ``(param_name, param_value) -> transformed_value``.
+        Useful for defining scalar test quantities (e.g.
+        ``lambda param_name, param_value: np.mean(param_value)`` to test the mean
+        of a vector parameter). The return values must be comparable with the ``<``
+        operator. The default is the identity (rank on the raw parameter values).
     keep_fits : bool, default True
         Whether to store posteriors to allow re-evaluation of rank statistics using
         a different quantity (``compute_rank_statistics``) without needing to run the
         simulations again.
 
-    Example
-    -------
+    Notes
+    -----
+    **Prior SBC** exploits the self-consistency of Bayesian updating:
+    if :math:`\theta' \sim \pi(\theta)` and
+    :math:`y' \sim \pi(y \mid \theta')`, then :math:`\theta'` is also
+    a draw from :math:`\pi(\theta \mid y')`.  See Talts et al., 2020 [1]_.
+
+    **Posterior SBC** uses the same self-consistency after conditioning
+    on observed data :math:`y_{\text{obs}}`.  A draw
+    :math:`\theta'_i \sim \pi(\theta \mid y_{\text{obs}})` and a
+    replicated dataset :math:`y_i \sim \pi(y \mid \theta'_i)` are
+    combined so that :math:`\theta'_i` is also a draw from
+    :math:`\pi(\theta \mid y_i, y_{\text{obs}})`.  The rank of
+    :math:`\theta'_i` among augmented-posterior draws should be
+    uniformly distributed if the inference is calibrated.
+    See Säilynoja et al., 2025 [2]_.
+
+    References
+    ----------
+    .. [1] Talts, S., Betancourt, M., Simpson, D., Vehtari, A., & Gelman, A.
+       (2020). Validating Bayesian Inference Algorithms with Simulation-Based
+       Calibration. arXiv:1804.06788.
+    .. [2] Säilynoja, T., Schmitt, M., Bürkner, P.-C., & Vehtari, A. (2025).
+       Posterior SBC: Simulation-Based Calibration Checking Conditional on
+       Data. arXiv:2502.03279.
+
+    Examples
+    --------
+    **Prior SBC** (default):
+
+    .. code-block:: python
 
-    .. code-block :: python
+        import pymc as pm
+        import simuk
 
         with pm.Model() as model:
             x = pm.Normal('x')
             y = pm.Normal('y', mu=2 * x, observed=obs)
 
-        sbc = SBC(model)
+        sbc = simuk.SBC(model, num_simulations=200)
+        sbc.run_simulations()
+
+    **Posterior SBC** – validate inference conditional on observed data:
+
+    .. code-block:: python
+
+        import pymc as pm
+        import simuk
+
+        with pm.Model() as model:
+            x = pm.Normal('x')
+            y = pm.Normal('y', mu=2 * x, observed=obs)
+
+            # 1. Obtain posterior samples from the real data
+            trace = pm.sample()
+
+        # 2. Run posterior SBC
+        sbc = simuk.SBC(
+            model,
+            method="posterior",
+            trace=trace,
+            num_simulations=200,
+        )
         sbc.run_simulations()
     """
 
     def __init__(
         self,
         model,
+        method="prior",
         num_simulations=1000,
         sample_kwargs=None,
         seed=None,
         data_dir=None,
         simulator=None,
+        trace=None,
+        augment_observed=None,
+        update_data=None,
         transform=None,
         keep_fits=True,
+        progress_bar=True,
     ):
         if hasattr(model, "basic_RVs") and isinstance(model, pm.Model):
             self.engine = "pymc"
@@ -121,7 +235,12 @@ def __init__(
             raise ValueError(
                 "model should be one of pymc.Model, bambi.Model, or numpyro.infer.mcmc.MCMCKernel"
             )
-        self.num_simulations = num_simulations
+
+        if method == "posterior" and self.engine != "pymc":
+            raise NotImplementedError("Currently, Posterior SBC is only implemented for PyMC")
+
+        self.progress_bar = progress_bar
+
         if sample_kwargs is None:
             sample_kwargs = {}
         if self.engine == "numpyro":
@@ -132,9 +251,12 @@ def __init__(
             sample_kwargs.setdefault("progressbar", False)
             sample_kwargs.setdefault("compute_convergence_checks", False)
         self.sample_kwargs = sample_kwargs
+
+        self.num_simulations = num_simulations
         self.seed = seed
         self._seeds = self._get_seeds()
-        self._extract_variable_names()
+
+        self._extract_model_info()
         self.simulations = {name: [] for name in self.var_names}
         self._simulations_complete = 0
         self.posteriors = []
@@ -152,9 +274,9 @@ def __init__(
             # Ideally, we could raise an error early for `numpyro` also,
             # but `factor` also produces 'observed_vars'
             raise ValueError(
-                "There are no observed variables, and PyMC will not generate prior "
-                "predictive samples. Either change the model or specify a simulator "
-                "with the `simulator` argument."
+                "There are no observed variables, and PyMC will not generate predictive "
+                "samples for both Prior and Posterior SBC. Either change the model or "
+                "specify a simulator with the `simulator` argument."
             )
 
         if simulator is None and self.engine == "numpyro":
@@ -175,11 +297,57 @@ def __init__(
         self._transform = lambda param_name, param_value: param_value
         if transform is not None:
             if not callable(transform):
-                raise ValueError("`param_transform` should be a function or None")
+                raise ValueError("`transform` should be a function or None")
             self._transform = transform
 
-    def _extract_variable_names(self):
-        """Extract observed and free variables from the model."""
+        self.method = method.lower()
+        if self.method == "posterior":
+            if trace is None:
+                raise ValueError(
+                    "When performing Posterior SBC, posterior samples from the "
+                    "original posterior are required to generate replicate datasets"
+                )
+            if "posterior" not in trace:
+                raise ValueError("`trace` should contain 'posterior' group")
+            if "observed_data" not in trace:
+                raise ValueError("`trace` should contain 'observed_data' group")
+            if self.num_simulations > trace["posterior"].sizes["draw"]:
+                raise ValueError(
+                    "posterior samples in `trace` should have more draws per "
+                    "chain than `num_simulations`. This is required to obtain enough "
+                    "posterior predictive samples"
+                )
+            self.trace = trace
+
+            if augment_observed is not None and not callable(augment_observed):
+                raise ValueError("`augment_observed` should be a function or None")
+            self.augment_observed = augment_observed
+
+            if update_data is not None and not callable(update_data):
+                raise ValueError("`update_data` should be a function or None")
+            self.update_data = update_data
+
+        else:
+            if update_data is not None:
+                logging.warning(
+                    "`update_data` is only supported for Posterior SBC. Ignoring...\n"
+                    "Prior SBC does not augment observations, so there is no need to "
+                    "update model data."
+                )
+            if augment_observed is not None:
+                logging.warning(
+                    "`augment_observed` is only supported for Posterior SBC. Ignoring...\n"
+                    "Prior SBC does not augment observations, so there is no need to "
+                    "augment observed data and replicated data"
+                )
+            if trace is not None:
+                logging.warning("`trace` is only used for Posterior SBC. Ignoring...")
+
+    def _extract_model_info(self):
+        """Extract observed and free variables from the model.
+
+        Also records the baseline state for Posterior SBC.
+        """
         if self.engine == "numpyro":
             self.model_params = set(inspect.signature(self.model).parameters.keys())
             with trace() as tr:
@@ -204,14 +372,77 @@ def _extract_variable_names(self):
             ]
 
         else:
-            self.observed_vars = [obs.name for obs in self.model.observed_RVs]
+            observed_var_nodes = [obs_rv for obs_rv in self.model.observed_RVs]
+            self.observed_vars = [obs.name for obs in observed_var_nodes]
             self.var_names = [v.name for v in self.model.free_RVs]
+            # Stores what observed values are given by pm.Data
+            self.observed_rvs_to_pm_data = {
+                var.name: (
+                    self.model.rvs_to_values[var].name
+                    if hasattr(self.model.rvs_to_values[var], "get_value")
+                    else None
+                )
+                for var in observed_var_nodes
+            }
+            self.model_baseline_state = self._get_baseline_state(self.model)
+
+    def _get_baseline_state(self, model):
+        """Extract the current mutable data and coordinates from a PyMC model."""
+        baseline_data = {}
+
+        # Extract Mutable Data
+        for var in model.data_vars:
+            if hasattr(var, "get_value"):
+                baseline_data[var.name] = var.get_value(borrow=False)
+
+        # Extract Coordinates
+        # Convert the internal PyMC coordinate object to a standard dictionary
+        baseline_coords = dict(model.coords)
+
+        return {"data": baseline_data, "coords": baseline_coords}
+
+    def _reset_model_state(self, model, model_state):
+        """Reset the state of PyMC model."""
+        with model:
+            pm.set_data(model_state["data"], coords=model_state["coords"])
 
     def _get_seeds(self):
         """Set the random seed, and generate seeds for all the simulations."""
         rng = np.random.default_rng(self.seed)
         return rng.integers(0, 2**30, size=self.num_simulations)
 
+    def _get_simulator_data(self, free_rv_samples):
+        """Run the user-defined simulator to obtain predictive samples.
+
+        These samples can be generated from either prior or posterior samples.
+        """
+        # Deal with custom simulator
+        pred = []
+        for i in range(free_rv_samples.sizes["sample"]):
+            params = {
+                var: free_rv_samples[var].isel(sample=i).values for var in free_rv_samples.data_vars
+            }
+            params["seed"] = self._seeds[i]
+            try:
+                res = self.simulator(**params)
+            except Exception as e:
+                raise ValueError(
+                    f"Error generating prior predictive sample with parameters {params}: {e}."
+                )
+
+            if not isinstance(res, Mapping):
+                raise TypeError(f"Simulator must return a dictionary, got {type(res)}")
+
+            pred.append(res)
+
+        pred = dict_to_dataset(
+            {key: np.stack([pp[key] for pp in pred]) for key in pred[0]},
+            sample_dims=["sample"],
+            coords={**free_rv_samples.coords},
+        )
+
+        return pred
+
     def _get_prior_predictive_samples(self):
         """Generate samples to use for the simulations."""
         with self.model:
@@ -219,29 +450,13 @@ def _get_prior_predictive_samples(self):
                 draws=self.num_simulations, random_seed=self._seeds[0]
             )
             prior = extract(idata, group="prior", keep_dataset=True)
+
             if self.simulator is None:
                 prior_pred = extract(idata, group="prior_predictive", keep_dataset=True)
                 return prior, prior_pred
-            # Deal with custom simulator
-            prior_pred = []
-            for i in range(prior.sizes["sample"]):
-                params = {var: prior[var].isel(sample=i).values for var in prior.data_vars}
-                params["seed"] = self._seeds[i]
-                try:
-                    res = self.simulator(**params)
-                    assert isinstance(res, Mapping), (
-                        f"Simulator must return a dictionary, got {type(res)}"
-                    )
-                    prior_pred.append(res)
-                except Exception as e:
-                    raise ValueError(
-                        f"Error generating prior predictive sample with parameters {params}: {e}."
-                    )
-            prior_pred = dict_to_dataset(
-                {key: np.stack([pp[key] for pp in prior_pred]) for key in prior_pred[0]},
-                sample_dims=["sample"],
-                coords={**prior.coords},
-            )
+
+            prior_pred = self._get_simulator_data(prior)
+
         return prior, prior_pred
 
     def _get_prior_predictive_samples_numpyro(self):
@@ -265,16 +480,78 @@ def _get_prior_predictive_samples_numpyro(self):
             prior_pred = {k: v for k, v in samples.items() if k in self.observed_model_vars}
         return prior, prior_pred
 
-    def _get_posterior_samples(self, prior_predictive_draw):
-        """Generate posterior samples conditioned to a prior predictive sample."""
-        new_model = pm.observe(self.model, prior_predictive_draw)
-        with new_model:
-            check = pm.sample(
-                **self.sample_kwargs,
-                random_seed=self._seeds[self._simulations_complete],
-            )
+    def _get_posterior_samples(self, replicated_data):
+        """Fit the model and return posterior draws for one SBC iteration.
+
+        For **Prior SBC** the model is conditioned on the replicated data
+        alone. For **Posterior SBC** the original observed data and the
+        replicated data are combined (via ``augment_observed`` or the default
+        simple concatenation) and the model is conditioned on the augmented
+        dataset.
+
+        Parameters
+        ----------
+        replicated_data : dict[str, np.ndarray]
+            Simulated observations for the current iteration, keyed by
+            observed-variable name.
+
+        Returns
+        -------
+        xarray.Dataset
+            Posterior draws from the (augmented) model.
+        """
+        if self.method == "posterior":
+            observed_data = self.trace["observed_data"]
+
+            if self.augment_observed is not None:
+                augmented_data = self.augment_observed(
+                    self.model, observed_data, replicated_data, self._simulations_complete
+                )
+            else:
+                # Default: concatenate original and replicated observations
+                augmented_data = {
+                    var_name: np.concatenate(
+                        [observed_data[var_name].values, replicated_data[var_name]]
+                    )
+                    for var_name in self.observed_vars
+                }
+
+            if self.update_data is not None:
+                with self.model:
+                    self.update_data(self.model, augmented_data, self._simulations_complete)
+
+            vars_to_observations = augmented_data
+        else:
+            # Prior SBC simply uses the generated prior predictive replicated data
+            vars_to_observations = replicated_data
+
+        # Set observed data that are pm.Data objects if the user hasn't modified them yet.
+        # We enforce an np.array_equal check against the baseline to prevent PyMC size mismatch
+        # ValueErrors when the user's `update_data` hook or `pm.observe` already updated it.
+        with self.model:
+            for rv, data_node in self.observed_rvs_to_pm_data.items():
+                if data_node is not None and np.array_equal(
+                    self.model.named_vars[data_node].get_value(),
+                    self.model_baseline_state["data"][data_node],
+                ):
+                    pm.set_data(new_data={data_node: vars_to_observations[rv]})
+
+        try:
+            new_model = pm.observe(self.model, vars_to_observations=vars_to_observations)
+            with new_model:
+                check = pm.sample(
+                    **self.sample_kwargs, random_seed=self._seeds[self._simulations_complete]
+                )
+
+            posterior = extract(check, group="posterior", keep_dataset=True)
+        except Exception:
+            traceback.print_exc()
+            raise
+        finally:
+            # Always ensure the model is reset to its un-augmented baseline state
+            # so the next simulation iteration isn't corrupted by the previous loop's augmented data
+            self._reset_model_state(self.model, self.model_baseline_state)
 
-        posterior = extract(check, group="posterior", keep_dataset=True)
         return posterior
 
     def _get_posterior_samples_numpyro(self, prior_predictive_draw):
@@ -293,9 +570,42 @@ def _get_posterior_samples_numpyro(self, prior_predictive_draw):
         mcmc.run(rng_seed, **free_vars_data, **prior_predictive_args)
         return from_numpyro(mcmc)["posterior"]
 
+    def _get_posterior_predictive_samples(self):
+        with self.model:
+            num_draws = self.trace["posterior"].sizes["draw"]
+            draw_indices = np.linspace(0, num_draws - 1, self.num_simulations, dtype=int)
+            thinned_idata = self.trace.isel(draw=draw_indices)
+            posterior = extract(thinned_idata, group="posterior", keep_dataset=True)
+
+            if self.simulator is None:
+                pm.sample_posterior_predictive(
+                    thinned_idata,
+                    extend_inferencedata=True,
+                    random_seed=self._seeds[0],
+                    progressbar=self.progress_bar,
+                )
+                posterior_pred = extract(
+                    thinned_idata, group="posterior_predictive", keep_dataset=True
+                )
+                return posterior, posterior_pred
+            else:
+                posterior_pred = self._get_simulator_data(posterior)
+
+            return posterior, posterior_pred
+
     def _convert_to_datatree(self):
+        """Pack the rank-statistic arrays into an xarray DataTree.
+
+        Creates a group named ``"prior_sbc"`` or ``"posterior_sbc"``
+        (depending on ``self.method``) inside ``self.simulations``.
+        """
+        if self.method == "prior":
+            group_name = "prior_sbc"
+        else:
+            group_name = "posterior_sbc"
+
         self.simulations = from_dict(
-            {"prior_sbc": self.simulations},
+            {group_name: self.simulations},
             attrs={
                 "/": {
                     "inferece_library": self.engine,
@@ -322,7 +632,7 @@ def compute_rank_statistics(self, transform=None):
             This function is applied to both the posterior draws and the
             reference parameter draws before computing the rank. For instance,
             it can be used to take the mean over a vectorized parameter grouping.
-            If None, defaults to the `param_transform` passed during class
+            If None, defaults to the `transform` passed during class
             initialization.
 
         Returns
@@ -378,28 +688,54 @@ def _compute_single_rank(self, simulation_idx, posterior, transform):
 
     @quiet_logging("pymc", "pytensor.gof.compilelock", "bambi")
     def run_simulations(self):
-        """Run all the simulations.
+        """Run all SBC iterations (Prior or Posterior SBC).
 
-        This function can be stopped and restarted on the same instance, so you can
-        keyboard interrupt part way through, look at the plot, and then resume. If a
-        seed was passed initially, it will still be respected (that is, the resulting
-        simulations will be identical to running without pausing in the middle).
-        """
-        prior, prior_pred = self._get_prior_predictive_samples()
-        self.ref_params = prior
+        For each iteration the method:
 
+        1. Draws a reference parameter vector and a replicated dataset
+           (from the prior / prior-predictive for Prior SBC, or from the
+           original posterior / posterior-predictive for Posterior SBC).
+        2. Fits the model to the (possibly augmented) replicated data.
+        3. Computes the rank of the reference draw among the new
+           (augmented) posterior draws.
+
+        The results are stored in ``self.simulations`` as an ArviZ
+        DataTree with group ``"prior_sbc"`` or ``"posterior_sbc"``.
+
+        This method can be stopped and restarted on the same instance:
+        you can keyboard-interrupt part way through, inspect the partial
+        results, and then call ``run_simulations()`` again to continue.
+        If a seed was passed at init, reproducibility is preserved.
+        """
         progress = tqdm(
             initial=self._simulations_complete,
             total=self.num_simulations,
+            disable=not self.progress_bar,
         )
 
+        if self.method == "prior":
+            # In Prior SBC, the reference parameter draws are from the prior,
+            # the predictive samples are from the prior predictive
+            ref_params, predictive = self._get_prior_predictive_samples()
+        else:
+            # In Posterior SBC, the reference parameter draws are from the original posterior,
+            # the predictive samples are from the original posterior predictive
+            ref_params, predictive = self._get_posterior_predictive_samples()
+
+        rng = np.random.default_rng(self.seed)
+        sample_indices = rng.choice(
+            ref_params.sizes["sample"], size=self.num_simulations, replace=False
+        )
+        self.ref_params = ref_params.isel(sample=sample_indices)
+        predictive = predictive.isel(sample=sample_indices)
+
         # if simulator is used, ignore observed_vars
         if self.simulator is not None:
-            self.observed_vars = list(prior_pred.data_vars)
+            self.observed_vars = list(predictive.data_vars)
             self.var_names = list(
                 filter(
                     lambda var_name: var_name not in self.observed_vars,
-                    list(prior.data_vars),
+                    list(ref_params.data_vars),
                 )
             )
             self.simulations = {var_name: [] for var_name in self.var_names}
@@ -407,12 +743,13 @@ def run_simulations(self):
         try:
             while self._simulations_complete < self.num_simulations:
                 idx = self._simulations_complete
-                prior_predictive_draw = {
-                    var_name: prior_pred[var_name].sel(chain=0, draw=idx).values
+
+                replicated_data = {
+                    var_name: predictive[var_name].isel(sample=idx).values
                     for var_name in self.observed_vars
                 }
 
-                posterior = self._get_posterior_samples(prior_predictive_draw)
+                posterior = self._get_posterior_samples(replicated_data)
                 if self.keep_fits:
                     self.posteriors.append(posterior)
                 else:
@@ -420,6 +757,9 @@ def run_simulations(self):
 
                 self._simulations_complete += 1
                 progress.update()
+        except Exception:
+            logging.error("Stopping simulation. An error occurred during simulations:")
+            traceback.print_exc()
         finally:
             if self._simulations_complete:
                 if self.keep_fits:
diff --git a/simuk/tests/test_posterior_sbc.py b/simuk/tests/test_posterior_sbc.py
new file mode 100644
index 0000000..5d2bb21
--- /dev/null
+++ b/simuk/tests/test_posterior_sbc.py
@@ -0,0 +1,330 @@
+"""Tests for Posterior SBC (method='posterior')."""
+
+import logging
+
+import numpy as np
+import numpyro.distributions as dist
+import pymc as pm
+import pytest
+from numpyro.infer import NUTS
+
+import simuk
+
+default_rng = np.random.default_rng(1234)
+
+# ---------------------------------------------------------------------------
+# Test data
+# ---------------------------------------------------------------------------
+
+obs_data = default_rng.normal(2.0, 1.0, size=20)
+x_obs = np.linspace(0, 1, 20)
+y_obs_reg = 1.5 * x_obs + default_rng.normal(0, 0.5, size=20)
+
+# ---------------------------------------------------------------------------
+# PyMC models and traces
+# ---------------------------------------------------------------------------
+
+with pm.Model() as simple_model:
+    mu = pm.Normal("mu", mu=0, sigma=5)
+    sigma = pm.HalfNormal("sigma", sigma=2)
+    y_data = pm.Data("y_data", obs_data)
+    pm.Normal("y", mu=mu, sigma=sigma, observed=y_data)
+
+with simple_model:
+    trace_simple = pm.sample(
+        draws=30,
+        tune=30,
+        chains=1,
+        random_seed=123,
+        progressbar=False,
+        compute_convergence_checks=False,
+    )
+
+coords = {"obs_id": np.arange(len(y_obs_reg))}
+with pm.Model(coords=coords) as reg_model:
+    x = pm.Data("x", x_obs, dims="obs_id")
+    y_data = pm.Data("y_data", y_obs_reg, dims="obs_id")
+    slope = pm.Normal("slope", mu=0, sigma=5)
+    sigma_reg = pm.HalfNormal("sigma", sigma=2)
+    pm.Normal("y", mu=slope * x, sigma=sigma_reg, observed=y_data, dims="obs_id")
+
+with reg_model:
+    trace_reg = pm.sample(
+        draws=30,
+        tune=30,
+        chains=1,
+        random_seed=123,
+        progressbar=False,
+        compute_convergence_checks=False,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Custom simulator and callback functions
+# ---------------------------------------------------------------------------
+
+
+def custom_simulator(mu, sigma, seed, **kwargs):
+    rng = np.random.default_rng(seed)
+    return {"y": rng.normal(mu, sigma, size=20)}
+
+
+def custom_augment_observed(model, observed_data, replicated_data, idx):
+    # Custom: only keep the last 10 original obs + all replicated
+    return {
+        var: np.concatenate([observed_data[var].values[-10:], replicated_data[var]])
+        for var in replicated_data
+    }
+
+
+def update_data_reg(model, augmented_data, idx):
+    """Resize covariates and coords to match augmented data."""
+    n_aug = len(augmented_data["y"])
+    x_aug = np.tile(x_obs, n_aug // len(x_obs) + 1)[:n_aug]
+    pm.set_data(
+        {"x": x_aug, "y_data": augmented_data["y"]},
+        coords={"obs_id": np.arange(n_aug)},
+    )
+
+
+def custom_transform(param_name, param_value):
+    return param_value**2
+
+
+# ---------------------------------------------------------------------------
+# Tests with observed variables
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("model,trace", [(simple_model, trace_simple)])
+def test_posterior_sbc_with_observed_data(model, trace):
+    """Basic posterior SBC with a PyMC model."""
+    sbc = simuk.SBC(
+        model,
+        method="posterior",
+        trace=trace,
+        num_simulations=2,
+        sample_kwargs={"draws": 5, "tune": 5},
+    )
+    sbc.run_simulations()
+    assert "posterior_sbc" in sbc.simulations
+
+
+@pytest.mark.parametrize("model,trace,update_data", [(reg_model, trace_reg, update_data_reg)])
+def test_posterior_sbc_with_update_data(model, trace, update_data):
+    """Posterior SBC with dims/coords and update_data callback."""
+    sbc = simuk.SBC(
+        model,
+        method="posterior",
+        trace=trace,
+        num_simulations=2,
+        sample_kwargs={"draws": 5, "tune": 5},
+        update_data=update_data,
+    )
+    sbc.run_simulations()
+    assert "posterior_sbc" in sbc.simulations
+
+
+# ---------------------------------------------------------------------------
+# Tests with custom simulator and callbacks
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("model,trace,simulator", [(simple_model, trace_simple, custom_simulator)])
+def test_posterior_sbc_with_custom_simulator(model, trace, simulator):
+    """Posterior SBC using a custom simulator function."""
+    sbc = simuk.SBC(
+        model,
+        method="posterior",
+        trace=trace,
+        num_simulations=2,
+        sample_kwargs={"draws": 5, "tune": 5},
+        simulator=simulator,
+    )
+    sbc.run_simulations()
+    assert "posterior_sbc" in sbc.simulations
+
+
+@pytest.mark.parametrize(
+    "model,trace,augment_observed",
+    [(simple_model, trace_simple, custom_augment_observed)],
+)
+def test_posterior_sbc_with_augment_observed(model, trace, augment_observed):
+    """Posterior SBC with a custom augment_observed callback."""
+    sbc = simuk.SBC(
+        model,
+        method="posterior",
+        trace=trace,
+        num_simulations=2,
+        sample_kwargs={"draws": 5, "tune": 5},
+        augment_observed=augment_observed,
+    )
+    sbc.run_simulations()
+    assert "posterior_sbc" in sbc.simulations
+
+
+@pytest.mark.parametrize(
+    "model,trace,transform",
+    [(simple_model, trace_simple, custom_transform)],
+)
+def test_posterior_sbc_with_transform(model, trace, transform):
+    """Posterior SBC with a transform(name, value) function."""
+    sbc = simuk.SBC(
+        model,
+        method="posterior",
+        trace=trace,
+        num_simulations=2,
+        sample_kwargs={"draws": 5, "tune": 5},
+        transform=transform,
+    )
+    sbc.run_simulations()
+    assert "posterior_sbc" in sbc.simulations
+
+
+# ---------------------------------------------------------------------------
+# Error-handling tests
+# ---------------------------------------------------------------------------
+
+
+def test_posterior_sbc_no_trace():
+    """method='posterior' without trace should raise ValueError."""
+    with pytest.raises(ValueError, match="posterior samples from the"):
+        simuk.SBC(
+            simple_model,
+            method="posterior",
+            num_simulations=5,
+            sample_kwargs={"draws": 5, "tune": 5},
+        )
+
+
+def test_posterior_sbc_trace_missing_posterior():
+    """trace without 'posterior' group should raise ValueError."""
+    trace_missing = trace_simple.copy()
+    del trace_missing["posterior"]
+    with pytest.raises(ValueError, match="posterior"):
+        simuk.SBC(
+            simple_model,
+            method="posterior",
+            trace=trace_missing,
+            num_simulations=5,
+            sample_kwargs={"draws": 5, "tune": 5},
+        )
+
+
+def test_posterior_sbc_trace_missing_observed_data():
+    """trace without 'observed_data' group should raise ValueError."""
+    trace_missing = trace_simple.copy()
+    del trace_missing["observed_data"]
+    with pytest.raises(ValueError, match="observed_data"):
+        simuk.SBC(
+            simple_model,
+            method="posterior",
+            trace=trace_missing,
+            num_simulations=5,
+            sample_kwargs={"draws": 5, "tune": 5},
+        )
+
+
+def test_posterior_sbc_too_many_simulations():
+    """num_simulations > draws should raise ValueError."""
+    with pytest.raises(ValueError, match="more draws per"):
+        simuk.SBC(
+            simple_model,
+            method="posterior",
+            trace=trace_simple,
+            num_simulations=100,  # trace_simple only has 30 draws
+            sample_kwargs={"draws": 5, "tune": 5},
+        )
+
+
+def test_posterior_sbc_numpyro_not_implemented():
+    """Posterior SBC is not yet implemented for NumPyro."""
+    numpyro = pytest.importorskip("numpyro")
+
+    def numpyro_model(y=None):
+        mu = numpyro.sample("mu", dist.Normal(0, 5))
+        numpyro.sample("y", dist.Normal(mu, 1), obs=y)
+
+    with pytest.raises(NotImplementedError, match="only implemented for PyMC"):
+        simuk.SBC(
+            NUTS(numpyro_model),
+            method="posterior",
+            trace=trace_simple,
+            data_dir={"y": obs_data},
+            num_simulations=5,
+        )
+
+
+def test_posterior_sbc_warnings_for_prior(caplog):
+    """Passing posterior-only args with method='prior' should emit warnings."""
+    with caplog.at_level(logging.WARNING):
+        simuk.SBC(
+            simple_model,
+            method="prior",
+            num_simulations=5,
+            sample_kwargs={"draws": 5, "tune": 5},
+            trace=trace_simple,
+            augment_observed=lambda *a: {},
+            update_data=lambda *a: None,
+        )
+
+    messages = caplog.text
+    assert "update_data" in messages
+    assert "augment_observed" in messages
+    assert "trace" in messages
+
+
+def test_posterior_sbc_update_data_not_callable():
+    """Passing a non-callable update_data should raise ValueError."""
+    with pytest.raises(ValueError, match="`update_data` should be a function or None"):
+        simuk.SBC(
+            simple_model,
+            method="posterior",
+            num_simulations=5,
+            sample_kwargs={"draws": 5, "tune": 5},
+            trace=trace_simple,
+            update_data="not a function",
+        )
+
+
+def test_posterior_sbc_augment_observed_not_callable():
+    """Passing a non-callable augment_observed should raise ValueError."""
+    with pytest.raises(ValueError, match="`augment_observed` should be a function or None"):
+        simuk.SBC(
+            simple_model,
+            method="posterior",
+            num_simulations=5,
+            sample_kwargs={"draws": 5, "tune": 5},
+            trace=trace_simple,
+            augment_observed="not a function",
+        )
+
+
+def test_posterior_sbc_bad_simulator_args():
+    """Any fault in executing the simulator should raise a general ValueError."""
+    with pytest.raises(ValueError, match="Error generating prior predictive sample"):
+        sbc = simuk.SBC(
+            simple_model,
+            method="posterior",
+            num_simulations=5,
+            sample_kwargs={"draws": 5, "tune": 5},
+            trace=trace_simple,
+            # bad function args
+            simulator=lambda: None,
+        )
+        sbc.run_simulations()
+
+
+def test_posterior_sbc_bad_simulator_return():
+    """Simulator should return a dictionary, otherwise raise a TypeError."""
+    with pytest.raises(TypeError, match="Simulator must return a dictionary"):
+        sbc = simuk.SBC(
+            simple_model,
+            method="posterior",
+            num_simulations=5,
+            sample_kwargs={"draws": 5, "tune": 5},
+            trace=trace_simple,
+            simulator=lambda *args, **kwargs: None,
+        )
+
+        sbc.run_simulations()
diff --git a/simuk/tests/test_sbc.py b/simuk/tests/test_prior_sbc.py
similarity index 97%
rename from simuk/tests/test_sbc.py
rename to simuk/tests/test_prior_sbc.py
index 86d945e..092cd41 100644
--- a/simuk/tests/test_sbc.py
+++ b/simuk/tests/test_prior_sbc.py
@@ -11,7 +11,7 @@
 
 import simuk
 
-np.random.seed(1234)
+default_rng = np.random.default_rng(1234)
 
 # Test data
 data = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])
@@ -31,8 +31,8 @@
     y_obs = pm.Normal("y", mu=theta, sigma=sigma)
 
 # Bambi model
-x = np.random.normal(0, 1, 20)
-y = 2 + np.random.normal(x, 1)
+x = default_rng.normal(0, 1, 20)
+y = 2 + default_rng.normal(x, 1)
 df = pd.DataFrame({"x": x, "y": y})
 bmb_model = bmb.Model("y ~ x", df)
 
@@ -229,7 +229,7 @@ def test_sbc_simulator_not_callable():
 
 
 def test_sbc_transform_not_callable_init():
-    with pytest.raises(ValueError, match="`param_transform` should be a function or None"):
+    with pytest.raises(ValueError, match="`transform` should be a function or None"):
         simuk.SBC(centered_eight, transform="not callable")