diff --git a/advanced/GP.ipynb b/advanced/GP.ipynb
index 9167dad..164ce4a 100644
--- a/advanced/GP.ipynb
+++ b/advanced/GP.ipynb
@@ -1,1208 +1,1199 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QX5PgBMVy8zw"
-      },
-      "source": [
-        "# Gaussian Process Regression\n",
-        "\n",
-        "Gaussian Processes are a non-parametric probabilistic modelling method that under some assumptions provide a flexible data model with hyperparameters that can be regressed to data in either an optimization or marginalization paradigm.\n",
-        "\n",
-        "An extension of the usual parameter inference of kernel hyperparameters employed in GPR is to consider a Bayesian Model Selection of the choice of Kernel itself {cite}`simpson2021marg, Kroupa_2024`.\n",
-        "\n",
-        "We will use this problem to demonstrate implementation of a model that has combinatorial degeneracy in it's parameters, and how to suppress this issue. Think _sorted_ priors or _forced identifiability transforms_ for those familiar with other popular NS implementations.\n",
-        "\n",
-        "As an aside we use `uv` in this case to do package management, which can be useful if using hosted notebooks"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "metadata": {
-        "id": "qa3e6JnjTnas"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install uv > /dev/null 2>&1\n",
-        "!uv pip install --system tqdm numpy jax anesthetic tinygp tensorflow_probability git+https://github.com/handley-lab/blackjax@nested_sampling > /dev/null 2>&1\n",
-        "import jax\n",
-        "import jax.numpy as jnp\n",
-        "import tinygp\n",
-        "import blackjax\n",
-        "import matplotlib.pyplot as plt\n",
-        "import tqdm\n",
-        "import tensorflow_probability.substrates.jax as tfp\n",
-        "from blackjax.ns.utils import finalise, sample\n",
-        "from functools import partial\n",
-        "\n",
-        "\n",
-        "tfd = tfp.distributions\n",
-        "tfb = tfp.bijectors\n",
-        "rng_key = jax.random.PRNGKey(0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "YXwEGf9RGdo_",
-        "outputId": "726f8908-d941-46a3-da96-935d404c69ff"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Running example on an NVIDIA L4 GPU\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(f\"Running example on an {jax.devices()[0].device_kind} GPU\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 860
-        },
-        "id": "sOSy2cACyUsr",
-        "outputId": "d6acfd31-e841-4328-aa2c-641bb8dfc59a"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<matplotlib.legend.Legend at 0x7a7720a01a50>"
-            ]
-          },
-          "execution_count": 22,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "\n",
-        "x_min = 0.0\n",
-        "x_max = 1.0\n",
-        "noise_level = 0.15\n",
-        "true_freqs = [3.0, 10]\n",
-        "x = jnp.linspace(x_min, x_max, 100)\n",
-        "\n",
-        "\n",
-        "def function(x, freqs):\n",
-        "    return jnp.sum(\n",
-        "        jnp.sin(2 * jnp.pi * x[:, None] * jnp.array(freqs)), axis=-1\n",
-        "    )\n",
-        "\n",
-        "rng_key, data_key = jax.random.split(rng_key)\n",
-        "y = (\n",
-        "    function(x, true_freqs)\n",
-        "    + jax.random.normal(data_key, x.shape[0]) * noise_level\n",
-        ")\n",
-        "x_plot=jnp.linspace(0,1,300)\n",
-        "f,a=plt.subplots()\n",
-        "a.plot(x_plot,function(x_plot, true_freqs[0]), c=\"C1\", label=\"basis 1\")\n",
-        "a.plot(x_plot,function(x_plot, true_freqs[1]), c=\"C2\", label=\"basis 2\")\n",
-        "\n",
-        "a.legend()\n",
-        "\n",
-        "f,a=plt.subplots()\n",
-        "\n",
-        "a.scatter(x,y, c=\"C0\", label=\"Data\")\n",
-        "\n",
-        "# plt.plot(x_plot,function(x_plot, true_freqs[0]), c=\"C1\", label=\"basis 1\")\n",
-        "# plt.plot(x_plot,function(x_plot, true_freqs[1]), c=\"C2\", label=\"basis 2\")\n",
-        "a.plot(x_plot,function(x_plot, true_freqs), c=\"C0\", label=\"Truth\")\n",
-        "a.legend()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "j_8ZfkxN--32"
-      },
-      "source": [
-        "## GP Likelihood and Priors\n",
-        "\n",
-        "We will use the `tinygp` library to provide the kernel and GP implementation, closely following the [Spectral Kernel Tutorial](https://tinygp.readthedocs.io/en/stable/benchmarks.html#benchmarks) on the `tinygp` docs. The Kernel function describing correlations on a length scale $\\tau$ is defined as,\n",
-        "\n",
-        "$$\n",
-        "k(\\tau) = \\sum^N_i w_i \\cos(2\\pi\\tau\\mu_{i}) \\exp (-2\\pi^2\\tau^2\\sigma_{i})\\,,\n",
-        "$$\n",
-        "Where we can identify a weight $w$ being applied to a Gaussian in fourier space at frequency $\\mu$ and with a variance $\\sigma$. There are then $N\\times3$ hyperparameters to fit, where $N$ is the number of degenerate basis functions we choose to paramaterize our distribution by. We will also introduce a global noise added to the diagonal of the covariance induced by the spectral kernel.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {
-        "id": "5XU8vl41yZfX"
-      },
-      "outputs": [],
-      "source": [
-        "class SpectralMixtureKernel(tinygp.kernels.Kernel):\n",
-        "    weight: jax.Array\n",
-        "    scale: jax.Array\n",
-        "    freq: jax.Array\n",
-        "\n",
-        "    def evaluate(self, X1, X2):\n",
-        "        tau = jnp.atleast_1d(jnp.abs(X1 - X2))[..., None]\n",
-        "        return jnp.sum(\n",
-        "            self.weight\n",
-        "            * jnp.prod(\n",
-        "                jnp.exp(-2 * jnp.pi**2 * tau * 2 / self.scale * 2)\n",
-        "                * jnp.cos(2 * jnp.pi * self.freq * tau),\n",
-        "                axis=0,\n",
-        "            )\n",
-        "        )\n",
-        "\n",
-        "def build_gp(params):\n",
-        "    kernel = SpectralMixtureKernel(\n",
-        "        jnp.exp(params[\"weight\"]),\n",
-        "        jnp.exp(params[\"scale\"]),\n",
-        "        params[\"freq\"],\n",
-        "    )\n",
-        "    process = tinygp.GaussianProcess(\n",
-        "        kernel,\n",
-        "        x,\n",
-        "        diag=jnp.exp(params[\"noise\"]),\n",
-        "    )\n",
-        "    return process\n",
-        "\n",
-        "\n",
-        "@jax.jit\n",
-        "def loglikelihood(theta):\n",
-        "    logl = build_gp(theta).log_probability(y)\n",
-        "    return logl"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xq6XQ4x1mdZI"
-      },
-      "source": [
-        "Now we declare a prior for this process, where we will use a Uniform prior over a reasonable range of frequencies and a lognormal prior on all other parameters. It is convenient to use a structured joint distribution to do this, and we use `tensorflow-probability` for this in this instance. To tell the sampler how to translate to this structure from a flat vector we will use the inbuilt `jax.flatten_util.ravel_pytree` to get the reverse of a tree flatten, so should match any internals of the `blackjax` code."
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QX5PgBMVy8zw"
+   },
+   "source": [
+    "# Gaussian Process Regression\n",
+    "\n",
+    "Gaussian Processes are a non-parametric probabilistic modelling method that under some assumptions provide a flexible data model with hyperparameters that can be regressed to data in either an optimization or marginalization paradigm.\n",
+    "\n",
+    "An extension of the usual parameter inference of kernel hyperparameters employed in GPR is to consider a Bayesian Model Selection of the choice of Kernel itself {cite}`simpson2021marg, Kroupa_2024`.\n",
+    "\n",
+    "We will use this problem to demonstrate implementation of a model that has combinatorial degeneracy in it's parameters, and how to suppress this issue. Think _sorted_ priors or _forced identifiability transforms_ for those familiar with other popular NS implementations.\n",
+    "\n",
+    "As an aside we use `uv` in this case to do package management, which can be useful if using hosted notebooks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "source": "!pip install git+https://github.com/handley-lab/blackjax\n!pip install tqdm numpy anesthetic tinygp tensorflow_probability matplotlib",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "source": "import jax\nimport jax.numpy as jnp\nimport tinygp\nimport blackjax\nimport matplotlib.pyplot as plt\nimport tqdm\nimport tensorflow_probability.substrates.jax as tfp\nfrom blackjax.ns.utils import finalise, sample\nfrom functools import partial\n\n\ntfd = tfp.distributions\ntfb = tfp.bijectors\nrng_key = jax.random.PRNGKey(0)",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "YXwEGf9RGdo_",
+    "outputId": "726f8908-d941-46a3-da96-935d404c69ff"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "mx3SuyCImesc"
-      },
-      "outputs": [],
-      "source": [
-        "fundamental_freq = 1 / (x.max() - x.min())\n",
-        "sampling_freq = x.shape[0] * fundamental_freq\n",
-        "\n",
-        "\n",
-        "def build_prior(n_components):\n",
-        "    # Define individual priors\n",
-        "    weight_prior = tfd.Normal(\n",
-        "        loc=jnp.zeros(n_components),\n",
-        "        scale=jnp.ones(n_components),\n",
-        "    )\n",
-        "    scale_prior = tfd.Normal(\n",
-        "            loc=jnp.zeros(n_components),\n",
-        "            scale=jnp.ones(n_components),\n",
-        "        )\n",
-        "    freq_prior = tfd.Uniform(\n",
-        "        low=jnp.ones(n_components) * fundamental_freq,\n",
-        "        high=jnp.ones(n_components) * sampling_freq / 2,\n",
-        "    )\n",
-        "\n",
-        "    noise_prior = tfd.Uniform(\n",
-        "        low=0.0,\n",
-        "        high=1.0,\n",
-        "    )\n",
-        "\n",
-        "    # Define joint distribution for sampling\n",
-        "    prior = tfd.JointDistributionNamed(\n",
-        "        {\n",
-        "            \"weight\": weight_prior,\n",
-        "            \"scale\": scale_prior,\n",
-        "            \"freq\": freq_prior,\n",
-        "            \"noise\": noise_prior,\n",
-        "        }\n",
-        "    )\n",
-        "\n",
-        "    # Manual log probability function\n",
-        "    def log_prob(params):\n",
-        "        \"\"\"Calculate the log probability of the parameters.\n",
-        "\n",
-        "        Args:\n",
-        "            params: Dictionary with keys 'weight', 'scale', 'freq', and 'noise'\n",
-        "\n",
-        "        Returns:\n",
-        "            Total log probability\n",
-        "        \"\"\"\n",
-        "        total_log_prob = 0.0\n",
-        "        total_log_prob += weight_prior.log_prob(params[\"weight\"]).sum()\n",
-        "        total_log_prob += freq_prior.log_prob(params[\"freq\"]).sum()\n",
-        "        total_log_prob += scale_prior.log_prob(params[\"scale\"]).sum()\n",
-        "        total_log_prob += noise_prior.log_prob(params[\"noise\"]).sum()\n",
-        "        return total_log_prob\n",
-        "\n",
-        "    return prior, log_prob\n",
-        "\n",
-        "\n",
-        "prior, log_prob = build_prior(n_components=2)\n"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running example on an NVIDIA L4 GPU\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Running example on an {jax.devices()[0].device_kind} GPU\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 860
     },
+    "id": "sOSy2cACyUsr",
+    "outputId": "d6acfd31-e841-4328-aa2c-641bb8dfc59a"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MO70iG-GmlJs"
-      },
-      "source": [
-        "## Sampling\n",
-        "\n",
-        "Finally we will pass all of this into the standard boilerplate Nested Sampling algorithm, where we 'oversample' the prior to hot start the sampler (technically the discarded points should be appended to the dead points but for brevity we omit this)."
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7a7720a01a50>"
       ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {
-        "id": "OMuXjAohmlXm"
-      },
-      "outputs": [],
-      "source": [
-        "test_sample, _= jax.flatten_util.ravel_pytree(\n",
-        "    prior.sample(seed=jax.random.PRNGKey(0))\n",
-        ")\n",
-        "n_dims = test_sample.shape[0]\n",
-        "n_initial = 10000\n",
-        "\n",
-        "n_live = 1000\n",
-        "n_delete = 500\n",
-        "\n",
-        "num_mcmc_steps = n_dims * 3\n",
-        "\n",
-        "# | Initialize the Nested Sampling algorithm\n",
-        "nested_sampler = blackjax.nss(\n",
-        "    logprior_fn=log_prob,\n",
-        "    loglikelihood_fn=loglikelihood,\n",
-        "    num_delete=n_delete,\n",
-        "    num_inner_steps=num_mcmc_steps,\n",
-        ")\n",
-        "\n",
-        "def integrate(nested_sampler,rng_key, sort=False):\n",
-        "    rng_key, init_key = jax.random.split(rng_key, 2)\n",
-        "    particles = prior.sample(seed=init_key, sample_shape=(n_initial,))\n",
-        "    if sort:\n",
-        "      idx = jnp.argsort(particles[\"freq\"])\n",
-        "      particles[\"freq\"] = jnp.take_along_axis(particles[\"freq\"], idx, -1)\n",
-        "      particles[\"weight\"] = jnp.take_along_axis(particles[\"weight\"], idx, -1)\n",
-        "      particles[\"scale\"] = jnp.take_along_axis(particles[\"scale\"], idx, -1)\n",
-        "    logl = jax.vmap(loglikelihood)(particles)\n",
-        "    top_logl, idx = jax.lax.top_k(logl, n_live)\n",
-        "    state = nested_sampler.init(\n",
-        "        jax.tree_util.tree_map(lambda leaf: leaf[idx], particles)\n",
-        "    )\n",
-        "\n",
-        "\n",
-        "    @jax.jit\n",
-        "    def one_step(carry, xs):\n",
-        "        state, k = carry\n",
-        "        k, subk = jax.random.split(k, 2)\n",
-        "        state, dead_point = nested_sampler.step(subk, state)\n",
-        "        return (state, k), dead_point\n",
-        "\n",
-        "    dead = []\n",
-        "    with tqdm.tqdm(desc=\"Dead points\", unit=\" dead points\") as pbar:\n",
-        "        while not state.logZ_live - state.logZ < -3:\n",
-        "            (state, rng_key), dead_info = one_step((state, rng_key), None)\n",
-        "            dead.append(dead_info)\n",
-        "            pbar.update(n_delete)\n",
-        "\n",
-        "    return state, finalise(state, dead)"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 26,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "_At6nxhXOpiL",
-        "outputId": "65fdb19f-7aa5-45d8-af89-3d04c67c7d1b"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Dead points: 20500 dead points [00:18, 1101.38 dead points/s]\n"
-          ]
-        }
-      ],
-      "source": [
-        "state, final = integrate(nested_sampler,rng_key)"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "x_min = 0.0\n",
+    "x_max = 1.0\n",
+    "noise_level = 0.15\n",
+    "true_freqs = [3.0, 10]\n",
+    "x = jnp.linspace(x_min, x_max, 100)\n",
+    "\n",
+    "\n",
+    "def function(x, freqs):\n",
+    "    return jnp.sum(\n",
+    "        jnp.sin(2 * jnp.pi * x[:, None] * jnp.array(freqs)), axis=-1\n",
+    "    )\n",
+    "\n",
+    "rng_key, data_key = jax.random.split(rng_key)\n",
+    "y = (\n",
+    "    function(x, true_freqs)\n",
+    "    + jax.random.normal(data_key, x.shape[0]) * noise_level\n",
+    ")\n",
+    "x_plot=jnp.linspace(0,1,300)\n",
+    "f,a=plt.subplots()\n",
+    "a.plot(x_plot,function(x_plot, true_freqs[0]), c=\"C1\", label=\"basis 1\")\n",
+    "a.plot(x_plot,function(x_plot, true_freqs[1]), c=\"C2\", label=\"basis 2\")\n",
+    "\n",
+    "a.legend()\n",
+    "\n",
+    "f,a=plt.subplots()\n",
+    "\n",
+    "a.scatter(x,y, c=\"C0\", label=\"Data\")\n",
+    "\n",
+    "# plt.plot(x_plot,function(x_plot, true_freqs[0]), c=\"C1\", label=\"basis 1\")\n",
+    "# plt.plot(x_plot,function(x_plot, true_freqs[1]), c=\"C2\", label=\"basis 2\")\n",
+    "a.plot(x_plot,function(x_plot, true_freqs), c=\"C0\", label=\"Truth\")\n",
+    "a.legend()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "j_8ZfkxN--32"
+   },
+   "source": [
+    "## GP Likelihood and Priors\n",
+    "\n",
+    "We will use the `tinygp` library to provide the kernel and GP implementation, closely following the [Spectral Kernel Tutorial](https://tinygp.readthedocs.io/en/stable/benchmarks.html#benchmarks) on the `tinygp` docs. The Kernel function describing correlations on a length scale $\\tau$ is defined as,\n",
+    "\n",
+    "$$\n",
+    "k(\\tau) = \\sum^N_i w_i \\cos(2\\pi\\tau\\mu_{i}) \\exp (-2\\pi^2\\tau^2\\sigma_{i})\\,,\n",
+    "$$\n",
+    "Where we can identify a weight $w$ being applied to a Gaussian in fourier space at frequency $\\mu$ and with a variance $\\sigma$. There are then $N\\times3$ hyperparameters to fit, where $N$ is the number of degenerate basis functions we choose to paramaterize our distribution by. We will also introduce a global noise added to the diagonal of the covariance induced by the spectral kernel.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "5XU8vl41yZfX"
+   },
+   "outputs": [],
+   "source": [
+    "class SpectralMixtureKernel(tinygp.kernels.Kernel):\n",
+    "    weight: jax.Array\n",
+    "    scale: jax.Array\n",
+    "    freq: jax.Array\n",
+    "\n",
+    "    def evaluate(self, X1, X2):\n",
+    "        tau = jnp.atleast_1d(jnp.abs(X1 - X2))[..., None]\n",
+    "        return jnp.sum(\n",
+    "            self.weight\n",
+    "            * jnp.prod(\n",
+    "                jnp.exp(-2 * jnp.pi**2 * tau * 2 / self.scale * 2)\n",
+    "                * jnp.cos(2 * jnp.pi * self.freq * tau),\n",
+    "                axis=0,\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "def build_gp(params):\n",
+    "    kernel = SpectralMixtureKernel(\n",
+    "        jnp.exp(params[\"weight\"]),\n",
+    "        jnp.exp(params[\"scale\"]),\n",
+    "        params[\"freq\"],\n",
+    "    )\n",
+    "    process = tinygp.GaussianProcess(\n",
+    "        kernel,\n",
+    "        x,\n",
+    "        diag=jnp.exp(params[\"noise\"]),\n",
+    "    )\n",
+    "    return process\n",
+    "\n",
+    "\n",
+    "@jax.jit\n",
+    "def loglikelihood(theta):\n",
+    "    logl = build_gp(theta).log_probability(y)\n",
+    "    return logl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xq6XQ4x1mdZI"
+   },
+   "source": [
+    "Now we declare a prior for this process, where we will use a Uniform prior over a reasonable range of frequencies and a lognormal prior on all other parameters. It is convenient to use a structured joint distribution to do this, and we use `tensorflow-probability` for this in this instance. To tell the sampler how to translate to this structure from a flat vector we will use the inbuilt `jax.flatten_util.ravel_pytree` to get the reverse of a tree flatten, so should match any internals of the `blackjax` code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "id": "mx3SuyCImesc"
+   },
+   "outputs": [],
+   "source": [
+    "fundamental_freq = 1 / (x.max() - x.min())\n",
+    "sampling_freq = x.shape[0] * fundamental_freq\n",
+    "\n",
+    "\n",
+    "def build_prior(n_components):\n",
+    "    # Define individual priors\n",
+    "    weight_prior = tfd.Normal(\n",
+    "        loc=jnp.zeros(n_components),\n",
+    "        scale=jnp.ones(n_components),\n",
+    "    )\n",
+    "    scale_prior = tfd.Normal(\n",
+    "            loc=jnp.zeros(n_components),\n",
+    "            scale=jnp.ones(n_components),\n",
+    "        )\n",
+    "    freq_prior = tfd.Uniform(\n",
+    "        low=jnp.ones(n_components) * fundamental_freq,\n",
+    "        high=jnp.ones(n_components) * sampling_freq / 2,\n",
+    "    )\n",
+    "\n",
+    "    noise_prior = tfd.Uniform(\n",
+    "        low=0.0,\n",
+    "        high=1.0,\n",
+    "    )\n",
+    "\n",
+    "    # Define joint distribution for sampling\n",
+    "    prior = tfd.JointDistributionNamed(\n",
+    "        {\n",
+    "            \"weight\": weight_prior,\n",
+    "            \"scale\": scale_prior,\n",
+    "            \"freq\": freq_prior,\n",
+    "            \"noise\": noise_prior,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "    # Manual log probability function\n",
+    "    def log_prob(params):\n",
+    "        \"\"\"Calculate the log probability of the parameters.\n",
+    "\n",
+    "        Args:\n",
+    "            params: Dictionary with keys 'weight', 'scale', 'freq', and 'noise'\n",
+    "\n",
+    "        Returns:\n",
+    "            Total log probability\n",
+    "        \"\"\"\n",
+    "        total_log_prob = 0.0\n",
+    "        total_log_prob += weight_prior.log_prob(params[\"weight\"]).sum()\n",
+    "        total_log_prob += freq_prior.log_prob(params[\"freq\"]).sum()\n",
+    "        total_log_prob += scale_prior.log_prob(params[\"scale\"]).sum()\n",
+    "        total_log_prob += noise_prior.log_prob(params[\"noise\"]).sum()\n",
+    "        return total_log_prob\n",
+    "\n",
+    "    return prior, log_prob\n",
+    "\n",
+    "\n",
+    "prior, log_prob = build_prior(n_components=2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MO70iG-GmlJs"
+   },
+   "source": [
+    "## Sampling\n",
+    "\n",
+    "Finally we will pass all of this into the standard boilerplate Nested Sampling algorithm, where we 'oversample' the prior to hot start the sampler (technically the discarded points should be appended to the dead points but for brevity we omit this)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "id": "OMuXjAohmlXm"
+   },
+   "outputs": [],
+   "source": [
+    "test_sample, _= jax.flatten_util.ravel_pytree(\n",
+    "    prior.sample(seed=jax.random.PRNGKey(0))\n",
+    ")\n",
+    "n_dims = test_sample.shape[0]\n",
+    "n_initial = 10000\n",
+    "\n",
+    "n_live = 1000\n",
+    "n_delete = 500\n",
+    "\n",
+    "num_mcmc_steps = n_dims * 3\n",
+    "\n",
+    "# | Initialize the Nested Sampling algorithm\n",
+    "nested_sampler = blackjax.nss(\n",
+    "    logprior_fn=log_prob,\n",
+    "    loglikelihood_fn=loglikelihood,\n",
+    "    num_delete=n_delete,\n",
+    "    num_inner_steps=num_mcmc_steps,\n",
+    ")\n",
+    "\n",
+    "def integrate(nested_sampler,rng_key, sort=False):\n",
+    "    rng_key, init_key = jax.random.split(rng_key, 2)\n",
+    "    particles = prior.sample(seed=init_key, sample_shape=(n_initial,))\n",
+    "    if sort:\n",
+    "      idx = jnp.argsort(particles[\"freq\"])\n",
+    "      particles[\"freq\"] = jnp.take_along_axis(particles[\"freq\"], idx, -1)\n",
+    "      particles[\"weight\"] = jnp.take_along_axis(particles[\"weight\"], idx, -1)\n",
+    "      particles[\"scale\"] = jnp.take_along_axis(particles[\"scale\"], idx, -1)\n",
+    "    logl = jax.vmap(loglikelihood)(particles)\n",
+    "    top_logl, idx = jax.lax.top_k(logl, n_live)\n",
+    "    state = nested_sampler.init(\n",
+    "        jax.tree_util.tree_map(lambda leaf: leaf[idx], particles)\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    @jax.jit\n",
+    "    def one_step(carry, xs):\n",
+    "        state, k = carry\n",
+    "        k, subk = jax.random.split(k, 2)\n",
+    "        state, dead_point = nested_sampler.step(subk, state)\n",
+    "        return (state, k), dead_point\n",
+    "\n",
+    "    dead = []\n",
+    "    with tqdm.tqdm(desc=\"Dead points\", unit=\" dead points\") as pbar:\n",
+    "        while not state.logZ_live - state.logZ < -3:\n",
+    "            (state, rng_key), dead_info = one_step((state, rng_key), None)\n",
+    "            dead.append(dead_info)\n",
+    "            pbar.update(n_delete)\n",
+    "\n",
+    "    return state, finalise(state, dead)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "_At6nxhXOpiL",
+    "outputId": "65fdb19f-7aa5-45d8-af89-3d04c67c7d1b"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "-F5bJTpnpNFr"
-      },
-      "source": [
-        "We can inspect the predicted form of the functional fit, shown crudely here with an averaged error bar on each individual process. And the value we quote for $\\ln Z$ can give us a method to compare to other kernels fit to this dataset. We additionally use Anesthetic to examine some example corner plots to diagnose the fitted parameter values, we will construct a reusable plot function for this"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Dead points: 20500 dead points [00:18, 1101.38 dead points/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "state, final = integrate(nested_sampler,rng_key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-F5bJTpnpNFr"
+   },
+   "source": [
+    "We can inspect the predicted form of the functional fit, shown crudely here with an averaged error bar on each individual process. And the value we quote for $\\ln Z$ can give us a method to compare to other kernels fit to this dataset. We additionally use Anesthetic to examine some example corner plots to diagnose the fitted parameter values, we will construct a reusable plot function for this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 430
     },
+    "id": "DKTjAfB1y8Gp",
+    "outputId": "dd5b0fd4-e45d-4f38-f9c4-05cc44d311ec"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 27,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 430
-        },
-        "id": "DKTjAfB1y8Gp",
-        "outputId": "dd5b0fd4-e45d-4f38-f9c4-05cc44d311ec"
-      },
-      "outputs": [
-        {
-          "data": {
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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "@jax.jit\n",
-        "def predict(theta, x_new):\n",
-        "    return build_gp(theta).predict(y, x_new, return_var=True)\n",
-        "\n",
-        "\n",
-        "def plot(rng_key, final):\n",
-        "  rng_key, sample_key = jax.random.split(rng_key)\n",
-        "  posterior_samples = sample(rng_key, final, 100)\n",
-        "  x_new = jnp.linspace(x_min, x_max, 200)\n",
-        "  pred = partial(predict, x_new=x_new)\n",
-        "\n",
-        "  y_new, y_var_new = jax.vmap(pred)(posterior_samples)\n",
-        "\n",
-        "  f, a = plt.subplots()\n",
-        "  a.scatter(x, y, c=\"C0\", label=\"data\")\n",
-        "  a.plot(x_new, function(x_new, true_freqs), c=\"C0\", label=\"Truth\")\n",
-        "  a.plot(x_new, y_new.T, c=\"C1\")\n",
-        "  from matplotlib.lines import Line2D\n",
-        "  # Create a custom proxy artist for the GP samples\n",
-        "  custom_line = Line2D([0], [0], color=\"C1\", lw=2)\n",
-        "  # Combine all handles and labels for the legend\n",
-        "  handles, labels = a.get_legend_handles_labels()\n",
-        "  handles.append(custom_line)\n",
-        "  labels.append(\"GP mean Samples\")\n",
-        "\n",
-        "  example_mean_prediction = y_new.mean(axis=0)\n",
-        "  example_var_prediction = jnp.sqrt(y_var_new.mean(axis=0))\n",
-        "  upper_bound = example_mean_prediction + 2 * example_var_prediction\n",
-        "  lower_bound = example_mean_prediction - 2 * example_var_prediction\n",
-        "  a.fill_between(x_new, lower_bound, upper_bound, color=\"C1\", alpha=0.5, label=\"Uncertainty (±σ)\")\n",
-        "\n",
-        "\n",
-        "  # Create the combined legend\n",
-        "  a.legend(handles, labels)\n",
-        "  f.savefig(\"GP.png\")\n",
-        "\n",
-        "plot(rng_key,final)\n",
-        "\n"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "@jax.jit\n",
+    "def predict(theta, x_new):\n",
+    "    return build_gp(theta).predict(y, x_new, return_var=True)\n",
+    "\n",
+    "\n",
+    "def plot(rng_key, final):\n",
+    "  rng_key, sample_key = jax.random.split(rng_key)\n",
+    "  posterior_samples = sample(rng_key, final, 100)\n",
+    "  x_new = jnp.linspace(x_min, x_max, 200)\n",
+    "  pred = partial(predict, x_new=x_new)\n",
+    "\n",
+    "  y_new, y_var_new = jax.vmap(pred)(posterior_samples)\n",
+    "\n",
+    "  f, a = plt.subplots()\n",
+    "  a.scatter(x, y, c=\"C0\", label=\"data\")\n",
+    "  a.plot(x_new, function(x_new, true_freqs), c=\"C0\", label=\"Truth\")\n",
+    "  a.plot(x_new, y_new.T, c=\"C1\")\n",
+    "  from matplotlib.lines import Line2D\n",
+    "  # Create a custom proxy artist for the GP samples\n",
+    "  custom_line = Line2D([0], [0], color=\"C1\", lw=2)\n",
+    "  # Combine all handles and labels for the legend\n",
+    "  handles, labels = a.get_legend_handles_labels()\n",
+    "  handles.append(custom_line)\n",
+    "  labels.append(\"GP mean Samples\")\n",
+    "\n",
+    "  example_mean_prediction = y_new.mean(axis=0)\n",
+    "  example_var_prediction = jnp.sqrt(y_var_new.mean(axis=0))\n",
+    "  upper_bound = example_mean_prediction + 2 * example_var_prediction\n",
+    "  lower_bound = example_mean_prediction - 2 * example_var_prediction\n",
+    "  a.fill_between(x_new, lower_bound, upper_bound, color=\"C1\", alpha=0.5, label=\"Uncertainty (±σ)\")\n",
+    "\n",
+    "\n",
+    "  # Create the combined legend\n",
+    "  a.legend(handles, labels)\n",
+    "  f.savefig(\"GP.png\")\n",
+    "\n",
+    "plot(rng_key,final)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9IRbUaNRPfwI"
+   },
+   "source": [
+    "We can ask for two useful statistics as a product of a run now, the evidence which we can use to compare kernels, and the number of evaluations as a proxy for efficiency:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "NPOL-QY9PoKi",
+    "outputId": "c1d31f1e-2d2d-4af0-ec24-3de094f6bc63"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9IRbUaNRPfwI"
-      },
-      "source": [
-        "We can ask for two useful statistics as a product of a run now, the evidence which we can use to compare kernels, and the number of evaluations as a proxy for efficiency:"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "log evidence = -136.53\n",
+      "total evals = 2252788\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"log evidence = {state.logZ:.2f}\")\n",
+    "print(f\"total evals = {final.inner_kernel_info.info.evals.sum()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o88YOrp0QttU"
+   },
+   "source": [
+    "We can lastly plot the corner plot of the inferred frequencies and see if they mirror the known inserted values, However we see there is a problem in the parameterization of our model, we were fitting two degenerate basis functions and have ended up with 2 copies of the posterior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 550
     },
+    "id": "sBDAjgNO14RI",
+    "outputId": "9b82b096-3df3-413f-b4b9-91df47b88b6c"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 28,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "NPOL-QY9PoKi",
-        "outputId": "c1d31f1e-2d2d-4af0-ec24-3de094f6bc63"
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"MCMCSamples(posterior_samples[\\\"freq\\\"], columns=[r\\\"$\\\\mu_{}$\\\"\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"$\\\\mu_0$\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"OffDiagonalAxes(0.125,0.11;0.3875x0.385)\",\n          \"DiagonalAxes(0.125,0.495;0.3875x0.385)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"$\\\\mu_1$\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"DiagonalAxes(0.5125,0.11;0.3875x0.385)\",\n          \"OffDiagonalAxes(0.5125,0.495;0.3875x0.385)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "log evidence = -136.53\n",
-            "total evals = 2252788\n"
-          ]
-        }
+      "text/html": [
+       "\n",
+       "  <div id=\"df-93be505a-2416-404e-b0b2-e512f2177bd8\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$\\mu_0$</th>\n",
+       "      <th>$\\mu_1$</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>$\\mu_0$</th>\n",
+       "      <td>DiagonalAx...</td>\n",
+       "      <td>OffDiagona...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>$\\mu_1$</th>\n",
+       "      <td>OffDiagona...</td>\n",
+       "      <td>DiagonalAx...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93be505a-2416-404e-b0b2-e512f2177bd8')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-93be505a-2416-404e-b0b2-e512f2177bd8 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-93be505a-2416-404e-b0b2-e512f2177bd8');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    <div id=\"df-dcc0930c-de6f-406c-a222-21c4e1432490\">\n",
+       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dcc0930c-de6f-406c-a222-21c4e1432490')\"\n",
+       "                title=\"Suggest charts\"\n",
+       "                style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "      </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "      <script>\n",
+       "        async function quickchart(key) {\n",
+       "          const quickchartButtonEl =\n",
+       "            document.querySelector('#' + key + ' button');\n",
+       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "          try {\n",
+       "            const charts = await google.colab.kernel.invokeFunction(\n",
+       "                'suggestCharts', [key], {});\n",
+       "          } catch (error) {\n",
+       "            console.error('Error during call to suggestCharts:', error);\n",
+       "          }\n",
+       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "        }\n",
+       "        (() => {\n",
+       "          let quickchartButtonEl =\n",
+       "            document.querySelector('#df-dcc0930c-de6f-406c-a222-21c4e1432490 button');\n",
+       "          quickchartButtonEl.style.display =\n",
+       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "        })();\n",
+       "      </script>\n",
+       "    </div>\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "print(f\"log evidence = {state.logZ:.2f}\")\n",
-        "print(f\"total evals = {final.inner_kernel_info.info.evals.sum()}\")"
+      "text/plain": [
+       "               $\\mu_0$        $\\mu_1$\n",
+       "$\\mu_0$  DiagonalAx...  OffDiagona...\n",
+       "$\\mu_1$  OffDiagona...  DiagonalAx..."
       ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "o88YOrp0QttU"
-      },
-      "source": [
-        "We can lastly plot the corner plot of the inferred frequencies and see if they mirror the known inserted values, However we see there is a problem in the parameterization of our model, we were fitting two degenerate basis functions and have ended up with 2 copies of the posterior."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 550
-        },
-        "id": "sBDAjgNO14RI",
-        "outputId": "9b82b096-3df3-413f-b4b9-91df47b88b6c"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "summary": "{\n  \"name\": \"MCMCSamples(posterior_samples[\\\"freq\\\"], columns=[r\\\"$\\\\mu_{}$\\\"\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"$\\\\mu_0$\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"OffDiagonalAxes(0.125,0.11;0.3875x0.385)\",\n          \"DiagonalAxes(0.125,0.495;0.3875x0.385)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"$\\\\mu_1$\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"DiagonalAxes(0.5125,0.11;0.3875x0.385)\",\n          \"OffDiagonalAxes(0.5125,0.495;0.3875x0.385)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-93be505a-2416-404e-b0b2-e512f2177bd8 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-93be505a-2416-404e-b0b2-e512f2177bd8');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "    <div id=\"df-dcc0930c-de6f-406c-a222-21c4e1432490\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dcc0930c-de6f-406c-a222-21c4e1432490')\"\n",
-              "                title=\"Suggest charts\"\n",
-              "                style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "      </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "      <script>\n",
-              "        async function quickchart(key) {\n",
-              "          const quickchartButtonEl =\n",
-              "            document.querySelector('#' + key + ' button');\n",
-              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "          try {\n",
-              "            const charts = await google.colab.kernel.invokeFunction(\n",
-              "                'suggestCharts', [key], {});\n",
-              "          } catch (error) {\n",
-              "            console.error('Error during call to suggestCharts:', error);\n",
-              "          }\n",
-              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "        }\n",
-              "        (() => {\n",
-              "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-dcc0930c-de6f-406c-a222-21c4e1432490 button');\n",
-              "          quickchartButtonEl.style.display =\n",
-              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "        })();\n",
-              "      </script>\n",
-              "    </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "               $\\mu_0$        $\\mu_1$\n",
-              "$\\mu_0$  DiagonalAx...  OffDiagona...\n",
-              "$\\mu_1$  OffDiagona...  DiagonalAx..."
-            ]
-          },
-          "execution_count": 29,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 6 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "from anesthetic import MCMCSamples\n",
-        "posterior_samples = sample(rng_key, final, 500)\n",
-        "\n",
-        "MCMCSamples(posterior_samples[\"freq\"], columns=[r\"$\\mu_{}$\".format(i) for i in range(n_components)]).plot_2d()"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 6 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from anesthetic import MCMCSamples\n",
+    "posterior_samples = sample(rng_key, final, 500)\n",
+    "\n",
+    "MCMCSamples(posterior_samples[\"freq\"], columns=[r\"$\\mu_{}$\".format(i) for i in range(n_components)]).plot_2d()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "awATWryBy7X7"
+   },
+   "source": [
+    "# Label Switching (Forced identifiability)\n",
+    "\n",
+    "This is a good canvas to discuss how to approach the label switching problem. An illustration of this is visible in the corner plot of frequency where 2 identical modes are present. As we are fitting Gaussian basis functions, there is a $N!$ degeneracy in the parameter space. For $N=2$ this is not disasterous, but sampling will quickly become innefficient for higher numbers of basis functions.\n",
+    "\n",
+    "There are a few options to address this, forced identifiability is a common transform to map the uniform hypercube over frequency to a simplex space, however this can have unpleasant effects on posterior geometry. In this example we will take a simpler effective approach of simply enforcing a sort in the covariance tuning. To do this we will overwrite the tuning function in the `nss` algorithm to account for this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "r4S1abPjrHNl",
+    "outputId": "df0ade98-b862-435f-d761-8b633dd35409"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "awATWryBy7X7"
-      },
-      "source": [
-        "# Label Switching (Forced identifiability)\n",
-        "\n",
-        "This is a good canvas to discuss how to approach the label switching problem. An illustration of this is visible in the corner plot of frequency where 2 identical modes are present. As we are fitting Gaussian basis functions, there is a $N!$ degeneracy in the parameter space. For $N=2$ this is not disasterous, but sampling will quickly become innefficient for higher numbers of basis functions.\n",
-        "\n",
-        "There are a few options to address this, forced identifiability is a common transform to map the uniform hypercube over frequency to a simplex space, however this can have unpleasant effects on posterior geometry. In this example we will take a simpler effective approach of simply enforcing a sort in the covariance tuning. To do this we will overwrite the tuning function in the `nss` algorithm to account for this."
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Dead points: 20000 dead points [00:18, 1080.97 dead points/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def sorted_stepper(x, n, t):\n",
+    "    y = jax.tree.map(lambda x, n: x + t * n, x, n)\n",
+    "    idx = jnp.argsort(y[\"freq\"])\n",
+    "    y[\"freq\"] = jnp.take_along_axis(y[\"freq\"], idx, -1)\n",
+    "    y[\"weight\"] = jnp.take_along_axis(y[\"weight\"], idx, -1)\n",
+    "    y[\"scale\"] = jnp.take_along_axis(y[\"scale\"], idx, -1)\n",
+    "    return y\n",
+    "\n",
+    "nested_sampler = blackjax.nss(\n",
+    "    logprior_fn=lambda x: prior.log_prob(x).sum(),\n",
+    "    loglikelihood_fn=loglikelihood,\n",
+    "    num_delete=n_delete,\n",
+    "    num_inner_steps=num_mcmc_steps,\n",
+    "    stepper_fn = sorted_stepper\n",
+    "    )\n",
+    "\n",
+    "state, final = integrate(nested_sampler,rng_key, sort=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5drCwXsbZLpH"
+   },
+   "source": [
+    "We can reuse our previous plotting and evaluation. We take more evaluations but achieve a similar functional form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 464
     },
+    "id": "eDcwCE_hRxtz",
+    "outputId": "35188683-a52e-4ec5-d226-c4da5e70273e"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 30,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "r4S1abPjrHNl",
-        "outputId": "df0ade98-b862-435f-d761-8b633dd35409"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Dead points: 20000 dead points [00:18, 1080.97 dead points/s]\n"
-          ]
-        }
-      ],
-      "source": [
-        "def sorted_stepper(x, n, t):\n",
-        "    y = jax.tree.map(lambda x, n: x + t * n, x, n)\n",
-        "    idx = jnp.argsort(y[\"freq\"])\n",
-        "    y[\"freq\"] = jnp.take_along_axis(y[\"freq\"], idx, -1)\n",
-        "    y[\"weight\"] = jnp.take_along_axis(y[\"weight\"], idx, -1)\n",
-        "    y[\"scale\"] = jnp.take_along_axis(y[\"scale\"], idx, -1)\n",
-        "    return y\n",
-        "\n",
-        "nested_sampler = blackjax.nss(\n",
-        "    logprior_fn=lambda x: prior.log_prob(x).sum(),\n",
-        "    loglikelihood_fn=loglikelihood,\n",
-        "    num_delete=n_delete,\n",
-        "    num_inner_steps=num_mcmc_steps,\n",
-        "    stepper_fn = sorted_stepper\n",
-        "    )\n",
-        "\n",
-        "state, final = integrate(nested_sampler,rng_key, sort=True)\n"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "log evidence = -135.53\n",
+      "total evals = 2011645\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5drCwXsbZLpH"
-      },
-      "source": [
-        "We can reuse our previous plotting and evaluation. We take more evaluations but achieve a similar functional form."
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(rng_key,final)\n",
+    "print(f\"log evidence = {state.logZ:.2f}\")\n",
+    "print(f\"total evals = {final.inner_kernel_info.info.evals.sum()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4pG5G8b8ZVFd"
+   },
+   "source": [
+    "Importantly if we inspect the corner plot of the frequencies, it appears that we have succesfully identified a single mode with frequencies matching the input $\\mu_0=3.0,\\mu_1=10.0$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 550
     },
+    "id": "asMRxTgA2JPe",
+    "outputId": "80280dd5-4792-43d0-aff0-d499c0129327"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 464
-        },
-        "id": "eDcwCE_hRxtz",
-        "outputId": "35188683-a52e-4ec5-d226-c4da5e70273e"
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"MCMCSamples(posterior_samples[\\\"freq\\\"], columns=[r\\\"$\\\\mu_{}$\\\"\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"$\\\\mu_0$\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"OffDiagonalAxes(0.125,0.11;0.3875x0.385)\",\n          \"DiagonalAxes(0.125,0.495;0.3875x0.385)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"$\\\\mu_1$\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"DiagonalAxes(0.5125,0.11;0.3875x0.385)\",\n          \"OffDiagonalAxes(0.5125,0.495;0.3875x0.385)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "log evidence = -135.53\n",
-            "total evals = 2011645\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$\\mu_0$</th>\n",
+       "      <th>$\\mu_1$</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>$\\mu_0$</th>\n",
+       "      <td>DiagonalAx...</td>\n",
+       "      <td>OffDiagona...</td>\n",
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+       "    <tr>\n",
+       "      <th>$\\mu_1$</th>\n",
+       "      <td>OffDiagona...</td>\n",
+       "      <td>DiagonalAx...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afe8b8f3-2d06-4c6a-bc5d-7982c143859b')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
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+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-afe8b8f3-2d06-4c6a-bc5d-7982c143859b button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-afe8b8f3-2d06-4c6a-bc5d-7982c143859b');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
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+       "\n",
+       "\n",
+       "    <div id=\"df-582d0d83-b9ed-4c98-ae8f-9c45ae03767d\">\n",
+       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-582d0d83-b9ed-4c98-ae8f-9c45ae03767d')\"\n",
+       "                title=\"Suggest charts\"\n",
+       "                style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "      </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "      <script>\n",
+       "        async function quickchart(key) {\n",
+       "          const quickchartButtonEl =\n",
+       "            document.querySelector('#' + key + ' button');\n",
+       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "          try {\n",
+       "            const charts = await google.colab.kernel.invokeFunction(\n",
+       "                'suggestCharts', [key], {});\n",
+       "          } catch (error) {\n",
+       "            console.error('Error during call to suggestCharts:', error);\n",
+       "          }\n",
+       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "        }\n",
+       "        (() => {\n",
+       "          let quickchartButtonEl =\n",
+       "            document.querySelector('#df-582d0d83-b9ed-4c98-ae8f-9c45ae03767d button');\n",
+       "          quickchartButtonEl.style.display =\n",
+       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "        })();\n",
+       "      </script>\n",
+       "    </div>\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "plot(rng_key,final)\n",
-        "print(f\"log evidence = {state.logZ:.2f}\")\n",
-        "print(f\"total evals = {final.inner_kernel_info.info.evals.sum()}\")"
+      "text/plain": [
+       "               $\\mu_0$        $\\mu_1$\n",
+       "$\\mu_0$  DiagonalAx...  OffDiagona...\n",
+       "$\\mu_1$  OffDiagona...  DiagonalAx..."
       ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "4pG5G8b8ZVFd"
-      },
-      "source": [
-        "Importantly if we inspect the corner plot of the frequencies, it appears that we have succesfully identified a single mode with frequencies matching the input $\\mu_0=3.0,\\mu_1=10.0$"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 6 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 550
-        },
-        "id": "asMRxTgA2JPe",
-        "outputId": "80280dd5-4792-43d0-aff0-d499c0129327"
-      },
-      "outputs": [
-        {
-          "data": {
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-              "summary": "{\n  \"name\": \"MCMCSamples(posterior_samples[\\\"freq\\\"], columns=[r\\\"$\\\\mu_{}$\\\"\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"$\\\\mu_0$\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"OffDiagonalAxes(0.125,0.11;0.3875x0.385)\",\n          \"DiagonalAxes(0.125,0.495;0.3875x0.385)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"$\\\\mu_1$\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"DiagonalAxes(0.5125,0.11;0.3875x0.385)\",\n          \"OffDiagonalAxes(0.5125,0.495;0.3875x0.385)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-              "type": "dataframe"
-            },
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-afe8b8f3-2d06-4c6a-bc5d-7982c143859b button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-afe8b8f3-2d06-4c6a-bc5d-7982c143859b');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "    <div id=\"df-582d0d83-b9ed-4c98-ae8f-9c45ae03767d\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-582d0d83-b9ed-4c98-ae8f-9c45ae03767d')\"\n",
-              "                title=\"Suggest charts\"\n",
-              "                style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "      </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "      <script>\n",
-              "        async function quickchart(key) {\n",
-              "          const quickchartButtonEl =\n",
-              "            document.querySelector('#' + key + ' button');\n",
-              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "          try {\n",
-              "            const charts = await google.colab.kernel.invokeFunction(\n",
-              "                'suggestCharts', [key], {});\n",
-              "          } catch (error) {\n",
-              "            console.error('Error during call to suggestCharts:', error);\n",
-              "          }\n",
-              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "        }\n",
-              "        (() => {\n",
-              "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-582d0d83-b9ed-4c98-ae8f-9c45ae03767d button');\n",
-              "          quickchartButtonEl.style.display =\n",
-              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "        })();\n",
-              "      </script>\n",
-              "    </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "               $\\mu_0$        $\\mu_1$\n",
-              "$\\mu_0$  DiagonalAx...  OffDiagona...\n",
-              "$\\mu_1$  OffDiagona...  DiagonalAx..."
-            ]
-          },
-          "execution_count": 32,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 6 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "from anesthetic import MCMCSamples\n",
-        "posterior_samples = sample(rng_key, final, 500)\n",
-        "\n",
-        "MCMCSamples(posterior_samples[\"freq\"], columns=[r\"$\\mu_{}$\".format(i) for i in range(n_components)]).plot_2d()"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "L4",
-      "machine_shape": "hm",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "nested-sampling-book (3.11.6)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python",
-      "version": "3.11.6"
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
+   ],
+   "source": [
+    "from anesthetic import MCMCSamples\n",
+    "posterior_samples = sample(rng_key, final, 500)\n",
+    "\n",
+    "MCMCSamples(posterior_samples[\"freq\"], columns=[r\"$\\mu_{}$\".format(i) for i in range(n_components)]).plot_2d()"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "L4",
+   "machine_shape": "hm",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "nested-sampling-book (3.11.6)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
+  "language_info": {
+   "name": "python",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/advanced/RW_NS.ipynb b/advanced/RW_NS.ipynb
index e8f559a..c63b514 100644
--- a/advanced/RW_NS.ipynb
+++ b/advanced/RW_NS.ipynb
@@ -1,5 +1,26 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7c7c10oq0bx",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "Install the required dependencies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ibs4jedhvj",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install git+https://github.com/handley-lab/blackjax\n",
+    "!pip install tqdm anesthetic numpy"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "40a4be43",
diff --git a/basic/basic.ipynb b/basic/basic.ipynb
index d264d04..ffdc17a 100644
--- a/basic/basic.ipynb
+++ b/basic/basic.ipynb
@@ -2,16 +2,20 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "source": "# Walkthrough of Nested Sampling\n\nWe walk through the same components in the [Quickstart](quickstart.ipynb), to give more detail as to what the choices mean.\n\n## Problem definition\n\nWe show how to use the basic user interface for Nested Slice Sampling. We will set up a 20D problem involving a multivariate unit Gaussian $\\mathcal{N}(0,\\mathbf{I})$ prior, and a multivariate Gaussian likelihood with a randomized mean and covariance. The resulting posterior and normalizing constant is analytically known.",
    "metadata": {},
-   "source": [
-    "# Walkthrough of Nested Sampling\n",
-    "\n",
-    "We walk through the same components in the [Quickstart](quickstart.ipynb), to give more detail as to what the choices mean.\n",
-    "\n",
-    "## Problem definition\n",
-    "\n",
-    "We show how to use the basic user interface for Nested Slice Sampling. We will set up a 20D problem involving a multivariate unit Gaussian $\\mathcal{N}(0,\\mathbf{I})$ prior, and a multivariate Gaussian likelihood with a randomized mean and covariance. The resulting posterior and normalizing constant is analytically known."
-   ]
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": "!pip install git+https://github.com/handley-lab/blackjax\n!pip install anesthetic numpy tqdm matplotlib"
   },
   {
    "cell_type": "code",
@@ -463,4 +467,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/basic/line_fitting.ipynb b/basic/line_fitting.ipynb
index 2ac180d..5342363 100644
--- a/basic/line_fitting.ipynb
+++ b/basic/line_fitting.ipynb
@@ -16,15 +16,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0j955op4hr",
-   "metadata": {},
-   "source": [
-    "### Installation"
-   ]
+   "id": "sk93iv14d1a",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "wl6jvwv27m",
    "metadata": {
     "execution": {
@@ -34,20 +32,14 @@
      "shell.execute_reply": "2025-09-22T17:42:50.231409Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install -q git+https://github.com/handley-lab/blackjax anesthetic tqdm"
-   ]
+   "outputs": [],
+   "source": "!pip install -q git+https://github.com/handley-lab/blackjax anesthetic tqdm matplotlib distrax optax flax"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "z8k3wvxkqf",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "code",
@@ -958,4 +950,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/basic/quickstart.ipynb b/basic/quickstart.ipynb
index 998c426..0a2bf3b 100644
--- a/basic/quickstart.ipynb
+++ b/basic/quickstart.ipynb
@@ -2,12 +2,20 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "source": "# Quickstart\n\nAssuming some level of knowledge with the blackjax style, the following is a minimum working example of a basic sampling problem",
    "metadata": {},
-   "source": [
-    "# Quickstart\n",
-    "\n",
-    "Assuming some level of knowledge with the blackjax style, the following is a minimum working example of a basic sampling problem "
-   ]
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": "!pip install git+https://github.com/handley-lab/blackjax\n!pip install anesthetic numpy tqdm"
   },
   {
    "cell_type": "code",
@@ -128,4 +136,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/integrations/c.ipynb b/integrations/c.ipynb
index 3603062..ef4df7e 100644
--- a/integrations/c.ipynb
+++ b/integrations/c.ipynb
@@ -3,7 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# C\n\nThis example demonstrates how to use BlackJAX nested sampling with C implementations of likelihood and prior functions. The C code is compiled to a shared library and accessed via Python's `ctypes` library, with JAX's `pure_callback` providing the bridge.\n\n## Prerequisites\n\nInstall the required Python packages:\n```bash\npip install git+https://github.com/handley-lab/blackjax\npip install numpy tqdm\n```\n\nYou'll also need a C compiler (gcc) installed on your system.\n\n## Setup Instructions\n\n### 1. Create the C implementation\n\nFirst, create a file `model.c` with your likelihood and prior functions:"
+   "source": "# C\n\nThis example demonstrates how to use BlackJAX nested sampling with C implementations of likelihood and prior functions. The C code is compiled to a shared library and accessed via Python's `ctypes` library, with JAX's `pure_callback` providing the bridge.\n\nYou'll also need a C compiler (gcc) installed on your system.\n\n## Setup Instructions\n\n### 1. Create the C implementation\n\nFirst, create a file `model.c` with your likelihood and prior functions:"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
diff --git a/integrations/cpp.ipynb b/integrations/cpp.ipynb
index e4e4e51..c5381ea 100644
--- a/integrations/cpp.ipynb
+++ b/integrations/cpp.ipynb
@@ -3,7 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# C++\n\nThis example demonstrates how to use BlackJAX nested sampling with C++ implementations of likelihood and prior functions. The C++ code is compiled using pybind11 to create a Python module, with JAX's `pure_callback` providing the bridge.\n\n## Prerequisites\n\nInstall the required Python packages:\n```bash\npip install git+https://github.com/handley-lab/blackjax\npip install pybind11 numpy tqdm\n```\n\nYou'll also need a C++ compiler (g++) installed on your system.\n\n## Setup Instructions\n\n### 1. Create the C++ implementation\n\nFirst, create a file `model.cpp` with your likelihood and prior functions using pybind11:"
+   "source": "# C++\n\nThis example demonstrates how to use BlackJAX nested sampling with C++ implementations of likelihood and prior functions. The C++ code is compiled using pybind11 to create a Python module, with JAX's `pure_callback` providing the bridge.\n\nYou'll also need a C++ compiler (g++) installed on your system.\n\n## Setup Instructions\n\n### 1. Create the C++ implementation\n\nFirst, create a file `model.cpp` with your likelihood and prior functions using pybind11:"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
diff --git a/integrations/cupy.ipynb b/integrations/cupy.ipynb
index 58d0644..9ff88de 100644
--- a/integrations/cupy.ipynb
+++ b/integrations/cupy.ipynb
@@ -3,7 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# CuPy\n\nThis example demonstrates how to use BlackJAX nested sampling with CuPy, the GPU-accelerated drop-in replacement for NumPy. CuPy enables GPU computation for likelihood and prior functions while maintaining NumPy-like syntax.\n\n## Prerequisites\n\nInstall the required packages:\n```bash\npip install git+https://github.com/handley-lab/blackjax\npip install cupy-cuda12x numpy tqdm  # Or cupy-cuda11x for CUDA 11.x\n```\n\nNote: CuPy requires CUDA. Choose the appropriate CuPy package for your CUDA version.\n\n## Run nested sampling with CuPy functions"
+   "source": "# CuPy\n\nThis example demonstrates how to use BlackJAX nested sampling with CuPy, the GPU-accelerated drop-in replacement for NumPy. CuPy enables GPU computation for likelihood and prior functions while maintaining NumPy-like syntax.\n\nNote: CuPy requires CUDA. Choose the appropriate CuPy package for your CUDA version.\n\n## Run nested sampling with CuPy functions"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "code",
diff --git a/integrations/fortran.ipynb b/integrations/fortran.ipynb
index b821c9a..cfeb272 100644
--- a/integrations/fortran.ipynb
+++ b/integrations/fortran.ipynb
@@ -3,7 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# Fortran\n\nThis example demonstrates how to use BlackJAX nested sampling with Fortran implementations of likelihood and prior functions. The Fortran code is compiled to a shared library and accessed via Python's `ctypes` library, with JAX's `pure_callback` providing the bridge.\n\n## Prerequisites\n\nInstall the required Python packages:\n```bash\npip install git+https://github.com/handley-lab/blackjax\npip install numpy tqdm\n```\n\nYou'll also need a Fortran compiler (gfortran) installed on your system.\n\n## Setup Instructions\n\n### 1. Create the Fortran implementation\n\nFirst, create a file `model.f90` with your likelihood and prior functions:"
+   "source": "# Fortran\n\nThis example demonstrates how to use BlackJAX nested sampling with Fortran implementations of likelihood and prior functions. The Fortran code is compiled to a shared library and accessed via Python's `ctypes` library, with JAX's `pure_callback` providing the bridge.\n\nYou'll also need a Fortran compiler (gfortran) installed on your system.\n\n## Setup Instructions\n\n### 1. Create the Fortran implementation\n\nFirst, create a file `model.f90` with your likelihood and prior functions:"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
diff --git a/integrations/julia.ipynb b/integrations/julia.ipynb
index fc41da7..1555d48 100644
--- a/integrations/julia.ipynb
+++ b/integrations/julia.ipynb
@@ -3,7 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# Julia\n\nThis example demonstrates how to use BlackJAX nested sampling with Julia implementations of likelihood and prior functions. Julia code is called via JSON-RPC in a separate process to avoid threading conflicts.\n\n## Prerequisites\n\nInstall the required Python packages:\n```bash\npip install git+https://github.com/handley-lab/blackjax\npip install numpy tqdm\n```\n\nInstall Julia and the required Julia packages:\n```julia\nusing Pkg\nPkg.add(\"LinearAlgebra\")\nPkg.add(\"Distributions\")\nPkg.add(\"JSON\")\n```\n\n## Setup Instructions\n\n### 1. Create the model file\n\nCreate a file `model.jl` with your likelihood and prior functions:\n\n```julia\nusing LinearAlgebra\nusing Distributions\n\nfunction loglikelihood(theta)\n    theta = convert(Matrix{Float64}, theta)\n    dist = MvNormal(ones(5), 0.01 * I(5))\n    return [logpdf(dist, theta[i, :]) for i in 1:size(theta, 1)]\nend\n\nfunction logprior(theta)\n    theta = convert(Matrix{Float64}, theta)\n    dist = MvNormal(zeros(5), I(5))\n    return [logpdf(dist, theta[i, :]) for i in 1:size(theta, 1)]\nend\n```\n\n### 2. Create the RPC server\n\nCreate a file `julia_server.jl` to handle RPC communication:\n\n```julia\nusing JSON\nusing Base64\n\n# Include the model functions\ninclude(\"model.jl\")\n\n# Simple request/response loop\nwhile true\n    try\n        line = readline()\n        if isempty(line)\n            break\n        end\n        \n        request = JSON.parse(line)\n        func_name = request[\"function\"]\n        \n        # Decode base64 numpy array\n        theta_bytes = base64decode(request[\"data\"])\n        theta = reinterpret(Float64, theta_bytes)\n        rows = request[\"rows\"]\n        cols = request[\"cols\"]\n        theta = reshape(theta, cols, rows)'  # Transpose for column-major\n        \n        # Call function\n        if func_name == \"loglikelihood\"\n            result = loglikelihood(theta)\n        elseif func_name == \"logprior\"\n            result = logprior(theta)\n        else\n            error(\"Unknown function: $func_name\")\n        end\n        \n        # Send response\n        response = Dict(\"result\" => result)\n        println(JSON.json(response))\n        flush(stdout)\n    catch e\n        println(JSON.json(Dict(\"error\" => string(e))))\n        flush(stdout)\n    end\nend\n```\n\n### 3. Run nested sampling with Julia functions"
+   "source": "# Julia\n\nThis example demonstrates how to use BlackJAX nested sampling with Julia implementations of likelihood and prior functions. Julia code is called via JSON-RPC in a separate process to avoid threading conflicts.\n\n## Setup Instructions\n\n### 1. Create the model file\n\nCreate a file `model.jl` with your likelihood and prior functions:\n\n```julia\nusing LinearAlgebra\nusing Distributions\n\nfunction loglikelihood(theta)\n    theta = convert(Matrix{Float64}, theta)\n    dist = MvNormal(ones(5), 0.01 * I(5))\n    return [logpdf(dist, theta[i, :]) for i in 1:size(theta, 1)]\nend\n\nfunction logprior(theta)\n    theta = convert(Matrix{Float64}, theta)\n    dist = MvNormal(zeros(5), I(5))\n    return [logpdf(dist, theta[i, :]) for i in 1:size(theta, 1)]\nend\n```\n\n### 2. Create the RPC server\n\nCreate a file `julia_server.jl` to handle RPC communication:\n\n```julia\nusing JSON\nusing Base64\n\n# Include the model functions\ninclude(\"model.jl\")\n\n# Simple request/response loop\nwhile true\n    try\n        line = readline()\n        if isempty(line)\n            break\n        end\n        \n        request = JSON.parse(line)\n        func_name = request[\"function\"]\n        \n        # Decode base64 numpy array\n        theta_bytes = base64decode(request[\"data\"])\n        theta = reinterpret(Float64, theta_bytes)\n        rows = request[\"rows\"]\n        cols = request[\"cols\"]\n        theta = reshape(theta, cols, rows)'  # Transpose for column-major\n        \n        # Call function\n        if func_name == \"loglikelihood\"\n            result = loglikelihood(theta)\n        elseif func_name == \"logprior\"\n            result = logprior(theta)\n        else\n            error(\"Unknown function: $func_name\")\n        end\n        \n        # Send response\n        response = Dict(\"result\" => result)\n        println(JSON.json(response))\n        flush(stdout)\n    catch e\n        println(JSON.json(Dict(\"error\" => string(e))))\n        flush(stdout)\n    end\nend\n```\n\n### 3. Run nested sampling with Julia functions"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "code",
diff --git a/integrations/numba.ipynb b/integrations/numba.ipynb
index 2e9d807..e191957 100644
--- a/integrations/numba.ipynb
+++ b/integrations/numba.ipynb
@@ -3,7 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# Numba\n\nThis example demonstrates how to use BlackJAX nested sampling with Numba JIT-compiled functions. Numba compiles Python functions to machine code at runtime, providing significant speedups for numerical computations while keeping the code in pure Python.\n\n## Prerequisites\n\nInstall the required packages:\n```bash\npip install git+https://github.com/handley-lab/blackjax\npip install numba numpy tqdm\n```\n\n## Run nested sampling with Numba functions"
+   "source": "# Numba\n\nThis example demonstrates how to use BlackJAX nested sampling with Numba JIT-compiled functions. Numba compiles Python functions to machine code at runtime, providing significant speedups for numerical computations while keeping the code in pure Python.\n\n## Run nested sampling with Numba functions"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "code",
diff --git a/integrations/numpy.ipynb b/integrations/numpy.ipynb
index 404e610..786e446 100644
--- a/integrations/numpy.ipynb
+++ b/integrations/numpy.ipynb
@@ -3,7 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# NumPy/SciPy\n\nThis example demonstrates how to use BlackJAX nested sampling with NumPy/SciPy distributions. SciPy provides a wide range of statistical distributions that can be used directly with BlackJAX through the wrapping pattern.\n\n## Prerequisites\n\nInstall the required packages:\n```bash\npip install git+https://github.com/handley-lab/blackjax\npip install scipy numpy tqdm\n```"
+   "source": "# NumPy/SciPy\n\nThis example demonstrates how to use BlackJAX nested sampling with NumPy/SciPy distributions. SciPy provides a wide range of statistical distributions that can be used directly with BlackJAX through the wrapping pattern."
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "code",
diff --git a/integrations/numpy_scipy.ipynb b/integrations/numpy_scipy.ipynb
index 4132784..7e660f7 100644
--- a/integrations/numpy_scipy.ipynb
+++ b/integrations/numpy_scipy.ipynb
@@ -3,7 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# NumPy/SciPy\n\nThis example demonstrates how to use BlackJAX nested sampling with NumPy and SciPy functions. This approach is useful when you have existing likelihood functions written in NumPy/SciPy that you want to use with BlackJAX.\n\n## Prerequisites\n\nInstall the required packages:\n```bash\npip install git+https://github.com/handley-lab/blackjax\npip install scipy numpy tqdm\n```\n\n## Run nested sampling with NumPy/SciPy functions"
+   "source": "# NumPy/SciPy\n\nThis example demonstrates how to use BlackJAX nested sampling with NumPy and SciPy functions. This approach is useful when you have existing likelihood functions written in NumPy/SciPy that you want to use with BlackJAX.\n\n## Run nested sampling with NumPy/SciPy functions"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "code",
diff --git a/integrations/pytorch.ipynb b/integrations/pytorch.ipynb
index 4f3d13f..154538c 100644
--- a/integrations/pytorch.ipynb
+++ b/integrations/pytorch.ipynb
@@ -3,7 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# PyTorch\n\nThis example demonstrates how to use BlackJAX nested sampling with PyTorch distributions and likelihood functions. The key is wrapping PyTorch functions to be compatible with JAX using `jax.pure_callback`.\n\n## Prerequisites\n\nInstall the required packages:\n```bash\npip install git+https://github.com/handley-lab/blackjax\npip install torch numpy tqdm\n```\n\n## Run nested sampling with PyTorch functions"
+   "source": "# PyTorch\n\nThis example demonstrates how to use BlackJAX nested sampling with PyTorch distributions and likelihood functions. The key is wrapping PyTorch functions to be compatible with JAX using `jax.pure_callback`.\n\n## Run nested sampling with PyTorch functions"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "source": "!pip install git+https://github.com/handley-lab/blackjax\n!pip install torch numpy tqdm",
+   "metadata": {}
   },
   {
    "cell_type": "code",
diff --git a/integrations/r.ipynb b/integrations/r.ipynb
index bf37a3e..5a77f05 100644
--- a/integrations/r.ipynb
+++ b/integrations/r.ipynb
@@ -3,7 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# R\n\nThis example demonstrates how to use BlackJAX nested sampling with R implementations of likelihood and prior functions. The R code is called via `rpy2` bridge, with JAX's `pure_callback` providing the interface.\n\n## Prerequisites\n\nInstall the required Python packages:\n```bash\npip install git+https://github.com/handley-lab/blackjax\npip install rpy2 numpy tqdm\n```\n\nInstall the required R package:\n```r\ninstall.packages('mvtnorm')\n```\n\n## Setup Instructions\n\n### 1. Create the R implementation\n\nCreate a file `model.R` with your likelihood and prior functions:\n\n```r\nlibrary(mvtnorm)\n\nloglikelihood <- function(theta) {\n    dmvnorm(theta, mean = rep(1, 5), sigma = 0.01 * diag(5), log = TRUE)\n}\n\nlogprior <- function(theta) {\n    dmvnorm(theta, mean = rep(0, 5), sigma = diag(5), log = TRUE)\n}\n```\n\n### 2. Run nested sampling with R functions"
+   "source": "# R\n\nThis example demonstrates how to use BlackJAX nested sampling with R implementations of likelihood and prior functions. The R code is called via `rpy2` bridge, with JAX's `pure_callback` providing the interface.\n\nInstall the required R package:\n```r\ninstall.packages('mvtnorm')\n```\n\n## Setup Instructions\n\n### 1. Create the R implementation\n\nCreate a file `model.R` with your likelihood and prior functions:\n\n```r\nlibrary(mvtnorm)\n\nloglikelihood <- function(theta) {\n    dmvnorm(theta, mean = rep(1, 5), sigma = 0.01 * diag(5), log = TRUE)\n}\n\nlogprior <- function(theta) {\n    dmvnorm(theta, mean = rep(0, 5), sigma = diag(5), log = TRUE)\n}\n```\n\n### 2. Run nested sampling with R functions"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "code",
diff --git a/integrations/tensorflow.ipynb b/integrations/tensorflow.ipynb
index 00f84cc..c55f536 100644
--- a/integrations/tensorflow.ipynb
+++ b/integrations/tensorflow.ipynb
@@ -3,7 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# TensorFlow\n\nThis example demonstrates how to use BlackJAX nested sampling with TensorFlow Probability distributions. TensorFlow Probability provides a rich library of probability distributions and statistical tools that can be integrated with BlackJAX.\n\n## Prerequisites\n\nInstall the required packages:\n```bash\npip install git+https://github.com/handley-lab/blackjax\npip install tensorflow tensorflow-probability numpy tqdm\n```\n\n## Run nested sampling with TensorFlow functions"
+   "source": "# TensorFlow\n\nThis example demonstrates how to use BlackJAX nested sampling with TensorFlow Probability distributions. TensorFlow Probability provides a rich library of probability distributions and statistical tools that can be integrated with BlackJAX.\n\n## Run nested sampling with TensorFlow functions"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
   },
   {
    "cell_type": "code",
diff --git a/physics/supernovae.ipynb b/physics/supernovae.ipynb
index d181f82..ec3b371 100644
--- a/physics/supernovae.ipynb
+++ b/physics/supernovae.ipynb
@@ -1,524 +1,519 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Supernova light curves\n",
-                "\n",
-                "In this example we will explore a physical example of fitting a supernova light curve model to data\n",
-                "\n",
-                "## Nested Sampling with JAX-bandflux\n",
-                "\n",
-                "This notebook demonstrates how to run the nested sampling procedure for supernovae SALT model fitting using the JAX-bandflux package (as implemented in `ns.py`). We will install the package, load the data, set up and run the nested sampling algorithm, and finally produce a corner plot of the posterior samples.\n",
-                "\n",
-                "For more examples and the complete codebase, visit the [JAX-bandflux GitHub repository](https://github.com/samleeney/JAX-bandflux). The academic paper associated with this work can be found [here](https://github.com/samleeney/JAX-bandflux/blob/71ca8d1b3b273147e1e9bf60a9ef11a806363b80/paper.bib)."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Install JAX-bandflux and its dependencies\n",
-                "!pip install jax-bandflux\n",
-                "\n",
-                "# Install BlackjaxNS and distrax\n",
-                "!pip install git+https://github.com/handley-lab/blackjax@proposal\n",
-                "!pip install git+https://github.com/google-deepmind/distrax\n",
-                "\n",
-                "# Additional dependencies\n",
-                "!pip install jax jaxlib anesthetic matplotlib tqdm"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Importing Libraries and Loading Data\n",
-                "\n",
-                "In this section, we import the required libraries and load the supernova light curve data using the function `load_and_process_data`. This function will also register the required bandpasses, process the data, and prepare it for modelling."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 41,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Loading data from /Users/yallup/projects/nested-sampling-book/env/lib/python3.12/site-packages/jax_supernovae/data/19dwz/all.phot\n",
-                        "Data loaded:\n",
-                        "Observation times shape: (33,)\n",
-                        "Flux measurements shape: (33,)\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import jax\n",
-                "import jax.numpy as jnp\n",
-                "import numpy as np\n",
-                "import matplotlib.pyplot as plt\n",
-                "import tqdm\n",
-                "\n",
-                "from blackjax.ns.utils import log_weights\n",
-                "from jax_supernovae.salt3 import optimized_salt3_multiband_flux\n",
-                "from jax_supernovae.data import load_and_process_data\n",
-                "from jax_supernovae.bandpasses import register_bandpass, get_bandpass, register_all_bandpasses\n",
-                "from jax_supernovae.utils import save_chains_dead_birth\n",
-                "from anesthetic import read_chains, make_2d_axes\n",
-                "import os\n",
-                "\n",
-                "# Set flag to use fixed redshift\n",
-                "fix_z = True\n",
-                "\n",
-                "# Load and process the data (ensure your data files are in the 'data' directory)\n",
-                "times, fluxes, fluxerrs, zps, band_indices, bridges, fixed_z = load_and_process_data('19dwz', data_dir='data', fix_z=fix_z)\n",
-                "\n",
-                "print('Data loaded:')\n",
-                "print('Observation times shape:', times.shape)\n",
-                "print('Flux measurements shape:', fluxes.shape)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Setting Up the Nested Sampling Algorithm\n",
-                "\n",
-                "Here we define the necessary functions for nested sampling. These include the log prior and log likelihood functions, which will be utilised by the sampling algorithm from Blackjax. The parameters being fitted are `t0`, `x0` (expressed in log scale), `x1`, `c` and optionally `log_sigma`. Prior bounds and distributions are defined accordingly."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Nested sampling settings\n",
-                "On a GPU, you might consider setting 'n_delete' to a value close to n_live/2, e.g.,\n",
-                "`NS_SETTINGS['n_delete'] = NS_SETTINGS['n_live'] // 2`. This change can substantially speedup the nested sampling algorithm."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 42,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Nested sampling settings\n",
-                "NS_SETTINGS = {\n",
-                "    'n_delete': 20,\n",
-                "    'n_live': 200,\n",
-                "    'num_mcmc_steps_multiplier': 5\n",
-                "}\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Setup prior bounds and distributions\n",
-                "In this section, we define the prior bounds and distributions for our SALT3 model parameters. The priors are uniform distributions over physically meaningful ranges for each parameter. For fixed redshift cases, we fit for `t0` (time of peak brightness), `x0` (overall amplitude), `x1` (light curve stretch), and `c` (colour). When redshift is not fixed, we also fit for `z` (redshift)."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 43,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import os\n",
-                "import distrax\n",
-                "import blackjax\n",
-                "\n",
-                "# Define prior bounds for parameters\n",
-                "PRIOR_BOUNDS = {\n",
-                "    't0': {'min': 58000.0, 'max': 59000.0},\n",
-                "    'x0': {'min': -5.0, 'max': -2.6},\n",
-                "    'x1': {'min': -4.0, 'max': 4.0},\n",
-                "    'c': {'min': -0.3, 'max': 0.3},\n",
-                "    'log_sigma': {'min': -3.0, 'max': 1.0}\n",
-                "}\n",
-                "\n",
-                "# Set whether to fit sigma (extra free parameter)\n",
-                "fit_sigma = False\n",
-                "\n",
-                "if fix_z:\n",
-                "    param_bounds = {\n",
-                "        't0': (PRIOR_BOUNDS['t0']['min'], PRIOR_BOUNDS['t0']['max']),\n",
-                "        'x0': (PRIOR_BOUNDS['x0']['min'], PRIOR_BOUNDS['x0']['max']),\n",
-                "        'x1': (PRIOR_BOUNDS['x1']['min'], PRIOR_BOUNDS['x1']['max']),\n",
-                "        'c': (PRIOR_BOUNDS['c']['min'], PRIOR_BOUNDS['c']['max'])\n",
-                "    }\n",
-                "    if fit_sigma:\n",
-                "        param_bounds['log_sigma'] = (PRIOR_BOUNDS['log_sigma']['min'], PRIOR_BOUNDS['log_sigma']['max'])\n",
-                "    prior_dists = {\n",
-                "        't0': distrax.Uniform(low=param_bounds['t0'][0], high=param_bounds['t0'][1]),\n",
-                "        'x0': distrax.Uniform(low=param_bounds['x0'][0], high=param_bounds['x0'][1]),\n",
-                "        'x1': distrax.Uniform(low=param_bounds['x1'][0], high=param_bounds['x1'][1]),\n",
-                "        'c': distrax.Uniform(low=param_bounds['c'][0], high=param_bounds['c'][1])\n",
-                "    }\n",
-                "    if fit_sigma:\n",
-                "        prior_dists['log_sigma'] = distrax.Uniform(low=param_bounds['log_sigma'][0], high=param_bounds['log_sigma'][1])\n",
-                "else:\n",
-                "    param_bounds = {\n",
-                "        'z': (0.001, 0.2),\n",
-                "        't0': (PRIOR_BOUNDS['t0']['min'], PRIOR_BOUNDS['t0']['max']),\n",
-                "        'x0': (PRIOR_BOUNDS['x0']['min'], PRIOR_BOUNDS['x0']['max']),\n",
-                "        'x1': (PRIOR_BOUNDS['x1']['min'], PRIOR_BOUNDS['x1']['max']),\n",
-                "        'c': (PRIOR_BOUNDS['c']['min'], PRIOR_BOUNDS['c']['max'])\n",
-                "    }\n",
-                "    if fit_sigma:\n",
-                "        param_bounds['log_sigma'] = (PRIOR_BOUNDS['log_sigma']['min'], PRIOR_BOUNDS['log_sigma']['max'])\n",
-                "    prior_dists = {\n",
-                "        'z': distrax.Uniform(low=param_bounds['z'][0], high=param_bounds['z'][1]),\n",
-                "        't0': distrax.Uniform(low=param_bounds['t0'][0], high=param_bounds['t0'][1]),\n",
-                "        'x0': distrax.Uniform(low=param_bounds['x0'][0], high=param_bounds['x0'][1]),\n",
-                "        'x1': distrax.Uniform(low=param_bounds['x1'][0], high=param_bounds['x1'][1]),\n",
-                "        'c': distrax.Uniform(low=param_bounds['c'][0], high=param_bounds['c'][1])\n",
-                "    }\n",
-                "    if fit_sigma:\n",
-                "        prior_dists['log_sigma'] = distrax.Uniform(low=param_bounds['log_sigma'][0], high=param_bounds['log_sigma'][1])\n",
-                "\n",
-                "@jax.jit\n",
-                "def logprior(params):\n",
-                "    if fix_z:\n",
-                "        if fit_sigma:\n",
-                "            logp = (prior_dists['t0'].log_prob(params[0]) +\n",
-                "                    prior_dists['x0'].log_prob(params[1]) +\n",
-                "                    prior_dists['x1'].log_prob(params[2]) +\n",
-                "                    prior_dists['c'].log_prob(params[3]) +\n",
-                "                    prior_dists['log_sigma'].log_prob(params[4]))\n",
-                "        else:\n",
-                "            logp = (prior_dists['t0'].log_prob(params[0]) +\n",
-                "                    prior_dists['x0'].log_prob(params[1]) +\n",
-                "                    prior_dists['x1'].log_prob(params[2]) +\n",
-                "                    prior_dists['c'].log_prob(params[3]))\n",
-                "    else:\n",
-                "        if fit_sigma:\n",
-                "            logp = (prior_dists['z'].log_prob(params[0]) +\n",
-                "                    prior_dists['t0'].log_prob(params[1]) +\n",
-                "                    prior_dists['x0'].log_prob(params[2]) +\n",
-                "                    prior_dists['x1'].log_prob(params[3]) +\n",
-                "                    prior_dists['c'].log_prob(params[4]) +\n",
-                "                    prior_dists['log_sigma'].log_prob(params[5]))\n",
-                "        else:\n",
-                "            logp = (prior_dists['z'].log_prob(params[0]) +\n",
-                "                    prior_dists['t0'].log_prob(params[1]) +\n",
-                "                    prior_dists['x0'].log_prob(params[2]) +\n",
-                "                    prior_dists['x1'].log_prob(params[3]) +\n",
-                "                    prior_dists['c'].log_prob(params[4]))\n",
-                "    return logp\n",
-                "# Define the log likelihood functions\n",
-                "@jax.jit\n",
-                "def compute_single_loglikelihood(params):\n",
-                "    if fix_z:\n",
-                "        if fit_sigma:\n",
-                "            t0, log_x0, x1, c, log_sigma = params\n",
-                "            sigma = 10 ** log_sigma\n",
-                "        else:\n",
-                "            t0, log_x0, x1, c = params\n",
-                "            sigma = 1.0\n",
-                "        z = fixed_z[0]\n",
-                "    else:\n",
-                "        if fit_sigma:\n",
-                "            z, t0, log_x0, x1, c, log_sigma = params\n",
-                "            sigma = 10 ** log_sigma\n",
-                "        else:\n",
-                "            z, t0, log_x0, x1, c = params\n",
-                "            sigma = 1.0\n",
-                "    x0 = 10 ** log_x0\n",
-                "    param_dict = {'z': z, 't0': t0, 'x0': x0, 'x1': x1, 'c': c}\n",
-                "    model_fluxes = optimized_salt3_multiband_flux(times, bridges, param_dict, zps=zps, zpsys='ab')\n",
-                "    model_fluxes = model_fluxes[jnp.arange(len(times)), band_indices]\n",
-                "    eff_fluxerrs = sigma * fluxerrs\n",
-                "    chi2 = jnp.sum(((fluxes - model_fluxes) / eff_fluxerrs) ** 2)\n",
-                "    log_likelihood = -0.5 * (chi2 + jnp.sum(jnp.log(2 * jnp.pi * eff_fluxerrs ** 2)))\n",
-                "    return log_likelihood\n",
-                "\n",
-                "def sample_from_priors(rng_key, n_samples):\n",
-                "    if fix_z:\n",
-                "        if fit_sigma:\n",
-                "            keys = jax.random.split(rng_key, 5)\n",
-                "            return jnp.column_stack([\n",
-                "                prior_dists['t0'].sample(seed=keys[0], sample_shape=(n_samples,)),\n",
-                "                prior_dists['x0'].sample(seed=keys[1], sample_shape=(n_samples,)),\n",
-                "                prior_dists['x1'].sample(seed=keys[2], sample_shape=(n_samples,)),\n",
-                "                prior_dists['c'].sample(seed=keys[3], sample_shape=(n_samples,)),\n",
-                "                prior_dists['log_sigma'].sample(seed=keys[4], sample_shape=(n_samples,))\n",
-                "            ])\n",
-                "        else:\n",
-                "            keys = jax.random.split(rng_key, 4)\n",
-                "            return jnp.column_stack([\n",
-                "                prior_dists['t0'].sample(seed=keys[0], sample_shape=(n_samples,)),\n",
-                "                prior_dists['x0'].sample(seed=keys[1], sample_shape=(n_samples,)),\n",
-                "                prior_dists['x1'].sample(seed=keys[2], sample_shape=(n_samples,)),\n",
-                "                prior_dists['c'].sample(seed=keys[3], sample_shape=(n_samples,))\n",
-                "            ])\n",
-                "    else:\n",
-                "        if fit_sigma:\n",
-                "            keys = jax.random.split(rng_key, 6)\n",
-                "            return jnp.column_stack([\n",
-                "                prior_dists['z'].sample(seed=keys[0], sample_shape=(n_samples,)),\n",
-                "                prior_dists['t0'].sample(seed=keys[1], sample_shape=(n_samples,)),\n",
-                "                prior_dists['x0'].sample(seed=keys[2], sample_shape=(n_samples,)),\n",
-                "                prior_dists['x1'].sample(seed=keys[3], sample_shape=(n_samples,)),\n",
-                "                prior_dists['c'].sample(seed=keys[4], sample_shape=(n_samples,)),\n",
-                "                prior_dists['log_sigma'].sample(seed=keys[5], sample_shape=(n_samples,))\n",
-                "            ])\n",
-                "        else:\n",
-                "            keys = jax.random.split(rng_key, 5)\n",
-                "            return jnp.column_stack([\n",
-                "                prior_dists['z'].sample(seed=keys[0], sample_shape=(n_samples,)),\n",
-                "                prior_dists['t0'].sample(seed=keys[1], sample_shape=(n_samples,)),\n",
-                "                prior_dists['x0'].sample(seed=keys[2], sample_shape=(n_samples,)),\n",
-                "                prior_dists['x1'].sample(seed=keys[3], sample_shape=(n_samples,)),\n",
-                "                prior_dists['c'].sample(seed=keys[4], sample_shape=(n_samples,))\n",
-                "            ])\n",
-                "\n",
-                "if fix_z:\n",
-                "    n_params_total = 4\n",
-                "else:\n",
-                "    n_params_total = 5\n",
-                "if fit_sigma:\n",
-                "    n_params_total += 1"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Initialise the nested sampling algorithm"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 44,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Setting up nested sampling algorithm...\n",
-                        "Initial particles generated, shape: (200, 4)\n"
-                    ]
-                }
-            ],
-            "source": [
-                "num_mcmc_steps = n_params_total * NS_SETTINGS['num_mcmc_steps_multiplier']\n",
-                "\n",
-                "# Initialise the nested sampling algorithm using Blackjax\n",
-                "print(\"Setting up nested sampling algorithm...\")\n",
-                "algo = blackjax.ns.adaptive.nss(\n",
-                "    logprior_fn=logprior,\n",
-                "    loglikelihood_fn=compute_single_loglikelihood,\n",
-                "    n_delete=NS_SETTINGS['n_delete'],\n",
-                "    num_mcmc_steps=num_mcmc_steps\n",
-                ")\n",
-                "\n",
-                "# Initialise random key and generate initial particles\n",
-                "rng_key = jax.random.PRNGKey(0)\n",
-                "rng_key, init_key = jax.random.split(rng_key)\n",
-                "\n",
-                "initial_particles = sample_from_priors(init_key, NS_SETTINGS['n_live'])\n",
-                "print(\"Initial particles generated, shape:\", initial_particles.shape)\n",
-                "\n",
-                "# Initialise the sampler state\n",
-                "state = algo.init(initial_particles, compute_single_loglikelihood)\n",
-                "\n",
-                "# Define a one-step function for the nested sampling (JIT compiled for efficiency)\n",
-                "@jax.jit\n",
-                "def one_step(carry, xs):\n",
-                "    state, k = carry\n",
-                "    k, subk = jax.random.split(k, 2)\n",
-                "    state, dead_point = algo.step(subk, state)\n",
-                "    return (state, k), dead_point"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Running the Nested Sampling\n",
-                "\n",
-                "Now we run the nested sampling algorithm for a specified number of iterations. The loop stops if the evidence of the live points is sufficiently lower than that of the dead points. Progress is printed every 10 iterations."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 45,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Running nested sampling...\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Dead points: 3020 dead points [03:52, 13.01 dead points/s]\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Runtime evidence: -160.40\n",
-                        "Estimated evidence: -161.47 ± 0.29\n"
-                    ]
-                }
-            ],
-            "source": [
-                "dead = []\n",
-                "# num_iterations = NS_SETTINGS['max_iterations']\n",
-                "\n",
-                "print(\"Running nested sampling...\")\n",
-                "with tqdm.tqdm(desc=\"Dead points\", unit=\" dead points\") as pbar:\n",
-                "    while (not state.sampler_state.logZ_live - state.sampler_state.logZ < -3):\n",
-                "        (state, rng_key), dead_info = one_step((state, rng_key), None)\n",
-                "        dead.append(dead_info)\n",
-                "        pbar.update(NS_SETTINGS['n_delete'])\n",
-                "    # if i % 10 == 0:\n",
-                "    #     print(f\"Iteration {i}: logZ = {state.sampler_state.logZ:.2f}\")\n",
-                "\n",
-                "# Combine dead points and compute log evidence\n",
-                "dead = jax.tree_map(lambda *args: jnp.concatenate(args), *dead)\n",
-                "logw = log_weights(rng_key, dead)\n",
-                "logZs = jax.scipy.special.logsumexp(logw, axis=0)\n",
-                "\n",
-                "print(f\"Runtime evidence: {state.sampler_state.logZ:.2f}\")\n",
-                "print(f\"Estimated evidence: {logZs.mean():.2f} ± {logZs.std():.2f}\")\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Post-Processing and Plotting\n",
-                "\n",
-                "After running the nested sampling procedure, we process the samples by saving the chains and creating a corner plot to visualise the posterior distributions. The `anesthetic` package is used for the 2D plots."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 46,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Saved 3020 samples to chains/chains_dead-birth.txt\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1000x1000 with 20 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Corner plot saved as 'corner_plot.png'.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# Define parameter names based on whether redshift is fixed or sigma is fit\n",
-                "if fix_z:\n",
-                "    param_names = ['t0', 'log_x0', 'x1', 'c']\n",
-                "else:\n",
-                "    param_names = ['z', 't0', 'log_x0', 'x1', 'c']\n",
-                "if fit_sigma:\n",
-                "    param_names.append('log_sigma')\n",
-                "\n",
-                "# Save the chains of dead points\n",
-                "save_chains_dead_birth(dead, param_names)\n",
-                "\n",
-                "# Read the chains using anesthetic\n",
-                "samples = read_chains('chains/chains', columns=param_names)\n",
-                "\n",
-                "# Create a corner plot of the posterior distributions\n",
-                "fig, axes = make_2d_axes(param_names, figsize=(10, 10), facecolor='w')\n",
-                "samples.plot_2d(axes, alpha=0.9, label=\"posterior\")\n",
-                "axes.iloc[-1, 0].legend(bbox_to_anchor=(len(axes)/2, len(axes)), loc='lower center', ncols=2)\n",
-                "\n",
-                "plt.savefig('corner_plot.png')\n",
-                "plt.show()\n",
-                "\n",
-                "print(\"Corner plot saved as 'corner_plot.png'.\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Parameter Statistics\n",
-                "\n",
-                "Finally, we print a summary of the fitted parameters, showing the mean and standard deviation for each parameter based on the posterior samples."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 47,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\n",
-                        "Parameter Statistics:\n",
-                        "--------------------------------------------------\n",
-                        "t0: 58606.500621 ± 0.636398\n",
-                        "log_x0: -2.967442 ± 0.023847\n",
-                        "x1: 1.440318 ± 0.704653\n",
-                        "c: 0.052617 ± 0.067823\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(\"\\nParameter Statistics:\")\n",
-                "print(\"-\" * 50)\n",
-                "for param in param_names:\n",
-                "    mean = samples[param].mean()\n",
-                "    std = samples[param].std()\n",
-                "    print(f\"{param}: {mean:.6f} ± {std:.6f}\")"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "env",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.12.5"
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Supernova light curves\n",
+    "\n",
+    "In this example we will explore a physical example of fitting a supernova light curve model to data\n",
+    "\n",
+    "## Nested Sampling with JAX-bandflux\n",
+    "\n",
+    "This notebook demonstrates how to run the nested sampling procedure for supernovae SALT model fitting using the JAX-bandflux package (as implemented in `ns.py`). We will install the package, load the data, set up and run the nested sampling algorithm, and finally produce a corner plot of the posterior samples.\n",
+    "\n",
+    "For more examples and the complete codebase, visit the [JAX-bandflux GitHub repository](https://github.com/samleeney/JAX-bandflux). The academic paper associated with this work can be found [here](https://github.com/samleeney/JAX-bandflux/blob/71ca8d1b3b273147e1e9bf60a9ef11a806363b80/paper.bib)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "# Install JAX-bandflux and its dependencies\n!pip install jax-bandflux\n\n# Install BlackjaxNS and distrax\n!pip install git+https://github.com/handley-lab/blackjax\n!pip install git+https://github.com/google-deepmind/distrax\n\n# Additional dependencies\n!pip install jax jaxlib anesthetic matplotlib tqdm"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing Libraries and Loading Data\n",
+    "\n",
+    "In this section, we import the required libraries and load the supernova light curve data using the function `load_and_process_data`. This function will also register the required bandpasses, process the data, and prepare it for modelling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading data from /Users/yallup/projects/nested-sampling-book/env/lib/python3.12/site-packages/jax_supernovae/data/19dwz/all.phot\n",
+      "Data loaded:\n",
+      "Observation times shape: (33,)\n",
+      "Flux measurements shape: (33,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tqdm\n",
+    "\n",
+    "from blackjax.ns.utils import log_weights\n",
+    "from jax_supernovae.salt3 import optimized_salt3_multiband_flux\n",
+    "from jax_supernovae.data import load_and_process_data\n",
+    "from jax_supernovae.bandpasses import register_bandpass, get_bandpass, register_all_bandpasses\n",
+    "from jax_supernovae.utils import save_chains_dead_birth\n",
+    "from anesthetic import read_chains, make_2d_axes\n",
+    "import os\n",
+    "\n",
+    "# Set flag to use fixed redshift\n",
+    "fix_z = True\n",
+    "\n",
+    "# Load and process the data (ensure your data files are in the 'data' directory)\n",
+    "times, fluxes, fluxerrs, zps, band_indices, bridges, fixed_z = load_and_process_data('19dwz', data_dir='data', fix_z=fix_z)\n",
+    "\n",
+    "print('Data loaded:')\n",
+    "print('Observation times shape:', times.shape)\n",
+    "print('Flux measurements shape:', fluxes.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting Up the Nested Sampling Algorithm\n",
+    "\n",
+    "Here we define the necessary functions for nested sampling. These include the log prior and log likelihood functions, which will be utilised by the sampling algorithm from Blackjax. The parameters being fitted are `t0`, `x0` (expressed in log scale), `x1`, `c` and optionally `log_sigma`. Prior bounds and distributions are defined accordingly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Nested sampling settings\n",
+    "On a GPU, you might consider setting 'n_delete' to a value close to n_live/2, e.g.,\n",
+    "`NS_SETTINGS['n_delete'] = NS_SETTINGS['n_live'] // 2`. This change can substantially speedup the nested sampling algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Nested sampling settings\n",
+    "NS_SETTINGS = {\n",
+    "    'n_delete': 20,\n",
+    "    'n_live': 200,\n",
+    "    'num_mcmc_steps_multiplier': 5\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup prior bounds and distributions\n",
+    "In this section, we define the prior bounds and distributions for our SALT3 model parameters. The priors are uniform distributions over physically meaningful ranges for each parameter. For fixed redshift cases, we fit for `t0` (time of peak brightness), `x0` (overall amplitude), `x1` (light curve stretch), and `c` (colour). When redshift is not fixed, we also fit for `z` (redshift)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import distrax\n",
+    "import blackjax\n",
+    "\n",
+    "# Define prior bounds for parameters\n",
+    "PRIOR_BOUNDS = {\n",
+    "    't0': {'min': 58000.0, 'max': 59000.0},\n",
+    "    'x0': {'min': -5.0, 'max': -2.6},\n",
+    "    'x1': {'min': -4.0, 'max': 4.0},\n",
+    "    'c': {'min': -0.3, 'max': 0.3},\n",
+    "    'log_sigma': {'min': -3.0, 'max': 1.0}\n",
+    "}\n",
+    "\n",
+    "# Set whether to fit sigma (extra free parameter)\n",
+    "fit_sigma = False\n",
+    "\n",
+    "if fix_z:\n",
+    "    param_bounds = {\n",
+    "        't0': (PRIOR_BOUNDS['t0']['min'], PRIOR_BOUNDS['t0']['max']),\n",
+    "        'x0': (PRIOR_BOUNDS['x0']['min'], PRIOR_BOUNDS['x0']['max']),\n",
+    "        'x1': (PRIOR_BOUNDS['x1']['min'], PRIOR_BOUNDS['x1']['max']),\n",
+    "        'c': (PRIOR_BOUNDS['c']['min'], PRIOR_BOUNDS['c']['max'])\n",
+    "    }\n",
+    "    if fit_sigma:\n",
+    "        param_bounds['log_sigma'] = (PRIOR_BOUNDS['log_sigma']['min'], PRIOR_BOUNDS['log_sigma']['max'])\n",
+    "    prior_dists = {\n",
+    "        't0': distrax.Uniform(low=param_bounds['t0'][0], high=param_bounds['t0'][1]),\n",
+    "        'x0': distrax.Uniform(low=param_bounds['x0'][0], high=param_bounds['x0'][1]),\n",
+    "        'x1': distrax.Uniform(low=param_bounds['x1'][0], high=param_bounds['x1'][1]),\n",
+    "        'c': distrax.Uniform(low=param_bounds['c'][0], high=param_bounds['c'][1])\n",
+    "    }\n",
+    "    if fit_sigma:\n",
+    "        prior_dists['log_sigma'] = distrax.Uniform(low=param_bounds['log_sigma'][0], high=param_bounds['log_sigma'][1])\n",
+    "else:\n",
+    "    param_bounds = {\n",
+    "        'z': (0.001, 0.2),\n",
+    "        't0': (PRIOR_BOUNDS['t0']['min'], PRIOR_BOUNDS['t0']['max']),\n",
+    "        'x0': (PRIOR_BOUNDS['x0']['min'], PRIOR_BOUNDS['x0']['max']),\n",
+    "        'x1': (PRIOR_BOUNDS['x1']['min'], PRIOR_BOUNDS['x1']['max']),\n",
+    "        'c': (PRIOR_BOUNDS['c']['min'], PRIOR_BOUNDS['c']['max'])\n",
+    "    }\n",
+    "    if fit_sigma:\n",
+    "        param_bounds['log_sigma'] = (PRIOR_BOUNDS['log_sigma']['min'], PRIOR_BOUNDS['log_sigma']['max'])\n",
+    "    prior_dists = {\n",
+    "        'z': distrax.Uniform(low=param_bounds['z'][0], high=param_bounds['z'][1]),\n",
+    "        't0': distrax.Uniform(low=param_bounds['t0'][0], high=param_bounds['t0'][1]),\n",
+    "        'x0': distrax.Uniform(low=param_bounds['x0'][0], high=param_bounds['x0'][1]),\n",
+    "        'x1': distrax.Uniform(low=param_bounds['x1'][0], high=param_bounds['x1'][1]),\n",
+    "        'c': distrax.Uniform(low=param_bounds['c'][0], high=param_bounds['c'][1])\n",
+    "    }\n",
+    "    if fit_sigma:\n",
+    "        prior_dists['log_sigma'] = distrax.Uniform(low=param_bounds['log_sigma'][0], high=param_bounds['log_sigma'][1])\n",
+    "\n",
+    "@jax.jit\n",
+    "def logprior(params):\n",
+    "    if fix_z:\n",
+    "        if fit_sigma:\n",
+    "            logp = (prior_dists['t0'].log_prob(params[0]) +\n",
+    "                    prior_dists['x0'].log_prob(params[1]) +\n",
+    "                    prior_dists['x1'].log_prob(params[2]) +\n",
+    "                    prior_dists['c'].log_prob(params[3]) +\n",
+    "                    prior_dists['log_sigma'].log_prob(params[4]))\n",
+    "        else:\n",
+    "            logp = (prior_dists['t0'].log_prob(params[0]) +\n",
+    "                    prior_dists['x0'].log_prob(params[1]) +\n",
+    "                    prior_dists['x1'].log_prob(params[2]) +\n",
+    "                    prior_dists['c'].log_prob(params[3]))\n",
+    "    else:\n",
+    "        if fit_sigma:\n",
+    "            logp = (prior_dists['z'].log_prob(params[0]) +\n",
+    "                    prior_dists['t0'].log_prob(params[1]) +\n",
+    "                    prior_dists['x0'].log_prob(params[2]) +\n",
+    "                    prior_dists['x1'].log_prob(params[3]) +\n",
+    "                    prior_dists['c'].log_prob(params[4]) +\n",
+    "                    prior_dists['log_sigma'].log_prob(params[5]))\n",
+    "        else:\n",
+    "            logp = (prior_dists['z'].log_prob(params[0]) +\n",
+    "                    prior_dists['t0'].log_prob(params[1]) +\n",
+    "                    prior_dists['x0'].log_prob(params[2]) +\n",
+    "                    prior_dists['x1'].log_prob(params[3]) +\n",
+    "                    prior_dists['c'].log_prob(params[4]))\n",
+    "    return logp\n",
+    "# Define the log likelihood functions\n",
+    "@jax.jit\n",
+    "def compute_single_loglikelihood(params):\n",
+    "    if fix_z:\n",
+    "        if fit_sigma:\n",
+    "            t0, log_x0, x1, c, log_sigma = params\n",
+    "            sigma = 10 ** log_sigma\n",
+    "        else:\n",
+    "            t0, log_x0, x1, c = params\n",
+    "            sigma = 1.0\n",
+    "        z = fixed_z[0]\n",
+    "    else:\n",
+    "        if fit_sigma:\n",
+    "            z, t0, log_x0, x1, c, log_sigma = params\n",
+    "            sigma = 10 ** log_sigma\n",
+    "        else:\n",
+    "            z, t0, log_x0, x1, c = params\n",
+    "            sigma = 1.0\n",
+    "    x0 = 10 ** log_x0\n",
+    "    param_dict = {'z': z, 't0': t0, 'x0': x0, 'x1': x1, 'c': c}\n",
+    "    model_fluxes = optimized_salt3_multiband_flux(times, bridges, param_dict, zps=zps, zpsys='ab')\n",
+    "    model_fluxes = model_fluxes[jnp.arange(len(times)), band_indices]\n",
+    "    eff_fluxerrs = sigma * fluxerrs\n",
+    "    chi2 = jnp.sum(((fluxes - model_fluxes) / eff_fluxerrs) ** 2)\n",
+    "    log_likelihood = -0.5 * (chi2 + jnp.sum(jnp.log(2 * jnp.pi * eff_fluxerrs ** 2)))\n",
+    "    return log_likelihood\n",
+    "\n",
+    "def sample_from_priors(rng_key, n_samples):\n",
+    "    if fix_z:\n",
+    "        if fit_sigma:\n",
+    "            keys = jax.random.split(rng_key, 5)\n",
+    "            return jnp.column_stack([\n",
+    "                prior_dists['t0'].sample(seed=keys[0], sample_shape=(n_samples,)),\n",
+    "                prior_dists['x0'].sample(seed=keys[1], sample_shape=(n_samples,)),\n",
+    "                prior_dists['x1'].sample(seed=keys[2], sample_shape=(n_samples,)),\n",
+    "                prior_dists['c'].sample(seed=keys[3], sample_shape=(n_samples,)),\n",
+    "                prior_dists['log_sigma'].sample(seed=keys[4], sample_shape=(n_samples,))\n",
+    "            ])\n",
+    "        else:\n",
+    "            keys = jax.random.split(rng_key, 4)\n",
+    "            return jnp.column_stack([\n",
+    "                prior_dists['t0'].sample(seed=keys[0], sample_shape=(n_samples,)),\n",
+    "                prior_dists['x0'].sample(seed=keys[1], sample_shape=(n_samples,)),\n",
+    "                prior_dists['x1'].sample(seed=keys[2], sample_shape=(n_samples,)),\n",
+    "                prior_dists['c'].sample(seed=keys[3], sample_shape=(n_samples,))\n",
+    "            ])\n",
+    "    else:\n",
+    "        if fit_sigma:\n",
+    "            keys = jax.random.split(rng_key, 6)\n",
+    "            return jnp.column_stack([\n",
+    "                prior_dists['z'].sample(seed=keys[0], sample_shape=(n_samples,)),\n",
+    "                prior_dists['t0'].sample(seed=keys[1], sample_shape=(n_samples,)),\n",
+    "                prior_dists['x0'].sample(seed=keys[2], sample_shape=(n_samples,)),\n",
+    "                prior_dists['x1'].sample(seed=keys[3], sample_shape=(n_samples,)),\n",
+    "                prior_dists['c'].sample(seed=keys[4], sample_shape=(n_samples,)),\n",
+    "                prior_dists['log_sigma'].sample(seed=keys[5], sample_shape=(n_samples,))\n",
+    "            ])\n",
+    "        else:\n",
+    "            keys = jax.random.split(rng_key, 5)\n",
+    "            return jnp.column_stack([\n",
+    "                prior_dists['z'].sample(seed=keys[0], sample_shape=(n_samples,)),\n",
+    "                prior_dists['t0'].sample(seed=keys[1], sample_shape=(n_samples,)),\n",
+    "                prior_dists['x0'].sample(seed=keys[2], sample_shape=(n_samples,)),\n",
+    "                prior_dists['x1'].sample(seed=keys[3], sample_shape=(n_samples,)),\n",
+    "                prior_dists['c'].sample(seed=keys[4], sample_shape=(n_samples,))\n",
+    "            ])\n",
+    "\n",
+    "if fix_z:\n",
+    "    n_params_total = 4\n",
+    "else:\n",
+    "    n_params_total = 5\n",
+    "if fit_sigma:\n",
+    "    n_params_total += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialise the nested sampling algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Setting up nested sampling algorithm...\n",
+      "Initial particles generated, shape: (200, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_mcmc_steps = n_params_total * NS_SETTINGS['num_mcmc_steps_multiplier']\n",
+    "\n",
+    "# Initialise the nested sampling algorithm using Blackjax\n",
+    "print(\"Setting up nested sampling algorithm...\")\n",
+    "algo = blackjax.ns.adaptive.nss(\n",
+    "    logprior_fn=logprior,\n",
+    "    loglikelihood_fn=compute_single_loglikelihood,\n",
+    "    n_delete=NS_SETTINGS['n_delete'],\n",
+    "    num_mcmc_steps=num_mcmc_steps\n",
+    ")\n",
+    "\n",
+    "# Initialise random key and generate initial particles\n",
+    "rng_key = jax.random.PRNGKey(0)\n",
+    "rng_key, init_key = jax.random.split(rng_key)\n",
+    "\n",
+    "initial_particles = sample_from_priors(init_key, NS_SETTINGS['n_live'])\n",
+    "print(\"Initial particles generated, shape:\", initial_particles.shape)\n",
+    "\n",
+    "# Initialise the sampler state\n",
+    "state = algo.init(initial_particles, compute_single_loglikelihood)\n",
+    "\n",
+    "# Define a one-step function for the nested sampling (JIT compiled for efficiency)\n",
+    "@jax.jit\n",
+    "def one_step(carry, xs):\n",
+    "    state, k = carry\n",
+    "    k, subk = jax.random.split(k, 2)\n",
+    "    state, dead_point = algo.step(subk, state)\n",
+    "    return (state, k), dead_point"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running the Nested Sampling\n",
+    "\n",
+    "Now we run the nested sampling algorithm for a specified number of iterations. The loop stops if the evidence of the live points is sufficiently lower than that of the dead points. Progress is printed every 10 iterations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running nested sampling...\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Dead points: 3020 dead points [03:52, 13.01 dead points/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Runtime evidence: -160.40\n",
+      "Estimated evidence: -161.47 ± 0.29\n"
+     ]
+    }
+   ],
+   "source": [
+    "dead = []\n",
+    "# num_iterations = NS_SETTINGS['max_iterations']\n",
+    "\n",
+    "print(\"Running nested sampling...\")\n",
+    "with tqdm.tqdm(desc=\"Dead points\", unit=\" dead points\") as pbar:\n",
+    "    while (not state.sampler_state.logZ_live - state.sampler_state.logZ < -3):\n",
+    "        (state, rng_key), dead_info = one_step((state, rng_key), None)\n",
+    "        dead.append(dead_info)\n",
+    "        pbar.update(NS_SETTINGS['n_delete'])\n",
+    "    # if i % 10 == 0:\n",
+    "    #     print(f\"Iteration {i}: logZ = {state.sampler_state.logZ:.2f}\")\n",
+    "\n",
+    "# Combine dead points and compute log evidence\n",
+    "dead = jax.tree_map(lambda *args: jnp.concatenate(args), *dead)\n",
+    "logw = log_weights(rng_key, dead)\n",
+    "logZs = jax.scipy.special.logsumexp(logw, axis=0)\n",
+    "\n",
+    "print(f\"Runtime evidence: {state.sampler_state.logZ:.2f}\")\n",
+    "print(f\"Estimated evidence: {logZs.mean():.2f} ± {logZs.std():.2f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Post-Processing and Plotting\n",
+    "\n",
+    "After running the nested sampling procedure, we process the samples by saving the chains and creating a corner plot to visualise the posterior distributions. The `anesthetic` package is used for the 2D plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved 3020 samples to chains/chains_dead-birth.txt\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Corner plot saved as 'corner_plot.png'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define parameter names based on whether redshift is fixed or sigma is fit\n",
+    "if fix_z:\n",
+    "    param_names = ['t0', 'log_x0', 'x1', 'c']\n",
+    "else:\n",
+    "    param_names = ['z', 't0', 'log_x0', 'x1', 'c']\n",
+    "if fit_sigma:\n",
+    "    param_names.append('log_sigma')\n",
+    "\n",
+    "# Save the chains of dead points\n",
+    "save_chains_dead_birth(dead, param_names)\n",
+    "\n",
+    "# Read the chains using anesthetic\n",
+    "samples = read_chains('chains/chains', columns=param_names)\n",
+    "\n",
+    "# Create a corner plot of the posterior distributions\n",
+    "fig, axes = make_2d_axes(param_names, figsize=(10, 10), facecolor='w')\n",
+    "samples.plot_2d(axes, alpha=0.9, label=\"posterior\")\n",
+    "axes.iloc[-1, 0].legend(bbox_to_anchor=(len(axes)/2, len(axes)), loc='lower center', ncols=2)\n",
+    "\n",
+    "plt.savefig('corner_plot.png')\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Corner plot saved as 'corner_plot.png'.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parameter Statistics\n",
+    "\n",
+    "Finally, we print a summary of the fitted parameters, showing the mean and standard deviation for each parameter based on the posterior samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Parameter Statistics:\n",
+      "--------------------------------------------------\n",
+      "t0: 58606.500621 ± 0.636398\n",
+      "log_x0: -2.967442 ± 0.023847\n",
+      "x1: 1.440318 ± 0.704653\n",
+      "c: 0.052617 ± 0.067823\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nParameter Statistics:\")\n",
+    "print(\"-\" * 50)\n",
+    "for param in param_names:\n",
+    "    mean = samples[param].mean()\n",
+    "    std = samples[param].std()\n",
+    "    print(f\"{param}: {mean:.6f} ± {std:.6f}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/sbi/nle_posterior.ipynb b/sbi/nle_posterior.ipynb
index 9450d94..1db4c51 100644
--- a/sbi/nle_posterior.ipynb
+++ b/sbi/nle_posterior.ipynb
@@ -3,27 +3,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "# Simulation Based Inference\n",
-    "\n",
-    "In this example we will go through an example setting up Nested Sampling as an external sampler for the `sbi` package. This uses the ability to integrate torch with `blackjax`, as detailed more in [PyTorch language integration](../integrations/pytorch.ipynb)\n",
-    "\n",
-    "## Neural Likelihood Estimation with Nested Sampling\n",
-    "\n",
-    "This example demonstrates how to use BlackJAX nested sampling as a posterior sampler for simulation-based inference (SBI) with neural likelihood estimation (NLE).\n",
-    "\n",
-    "## Prerequisites\n",
-    "\n",
-    "Install the required packages:\n",
-    "```bash\n",
-    "pip install git+https://github.com/handley-lab/blackjax\n",
-    "pip install sbi torch anesthetic numpy tqdm\n",
-    "```\n",
-    "\n",
-    "## NSPosterior Implementation\n",
-    "\n",
-    "First, we define a custom posterior class that uses BlackJAX nested sampling to sample from the posterior distribution given a trained likelihood estimator and prior."
-   ]
+   "source": "# Simulation Based Inference\n\nIn this example we will go through an example setting up Nested Sampling as an external sampler for the `sbi` package. This uses the ability to integrate torch with `blackjax`, as detailed more in [PyTorch language integration](../integrations/pytorch.ipynb)\n\n## Neural Likelihood Estimation with Nested Sampling\n\nThis example demonstrates how to use BlackJAX nested sampling as a posterior sampler for simulation-based inference (SBI) with neural likelihood estimation (NLE).\n\n## NSPosterior Implementation\n\nFirst, we define a custom posterior class that uses BlackJAX nested sampling to sample from the posterior distribution given a trained likelihood estimator and prior."
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## Prerequisites\n\nInstall the required dependencies:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "source": "!pip install git+https://github.com/handley-lab/blackjax\n!pip install sbi torch anesthetic numpy tqdm",
+   "metadata": {}
   },
   {
    "cell_type": "code",
@@ -361,4 +351,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file