diff --git a/notebooks/dirichlet-process.ipynb b/notebooks/dirichlet-process.ipynb
new file mode 100644
index 00000000..18879bdb
--- /dev/null
+++ b/notebooks/dirichlet-process.ipynb
@@ -0,0 +1,224 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ddde367f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymc as pm\n",
+    "import numpy as np\n",
+    "import pymc_experimental as pmx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45731819",
+   "metadata": {},
+   "source": [
+    "I follow **Example 9** from [here](https://projecteuclid.org/ebooks/nsf-cbms-regional-conference-series-in-probability-and-statistics/Nonparametric-Bayesian-Inference/Chapter/Chapter-3-Dirichlet-Process/10.1214/cbms/1362163748) (displayed below) and attempt to also replicate the results shown in Figure 3.2. They assume that data are drawn i.i.d. from $\\mathcal{N}(2, 4)$ but assume a base distribution $G_0 = \\mathcal{N}(0, 1)$.\n",
+    "\n",
+    "<img src=\"dp-example-9.png\" width=500>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "93713949",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = 5.0  # concentration parameter\n",
+    "K = 19  # truncation parameter\n",
+    "\n",
+    "rng = np.random.default_rng(seed=34)\n",
+    "obs = rng.normal(2.0, 2.0, size=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7fab0297",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [base_dist, sbw]\n",
+      ">BinaryGibbsMetropolis: [idx]\n",
+      ">CategoricalGibbsMetropolis: [atom_selection]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:07&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 7 seconds.\n",
+      "Chain <xarray.DataArray 'chain' ()>\n",
+      "array(0)\n",
+      "Coordinates:\n",
+      "    chain    int64 0 reached the maximum tree depth. Increase `max_treedepth`, increase `target_accept` or reparameterize.\n",
+      "Chain <xarray.DataArray 'chain' ()>\n",
+      "array(1)\n",
+      "Coordinates:\n",
+      "    chain    int64 1 reached the maximum tree depth. Increase `max_treedepth`, increase `target_accept` or reparameterize.\n",
+      "Chain <xarray.DataArray 'chain' ()>\n",
+      "array(2)\n",
+      "Coordinates:\n",
+      "    chain    int64 2 reached the maximum tree depth. Increase `max_treedepth`, increase `target_accept` or reparameterize.\n",
+      "Chain <xarray.DataArray 'chain' ()>\n",
+      "array(3)\n",
+      "Coordinates:\n",
+      "    chain    int64 3 reached the maximum tree depth. Increase `max_treedepth`, increase `target_accept` or reparameterize.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with pm.Model() as model:\n",
+    "    base_dist = pm.Normal(\"base_dist\", 0.0, 1.0, shape=(K + 1,))\n",
+    "    sbw, atoms = pmx.dp.DirichletProcess(\"dp\", alpha, base_dist, K, observed=obs)\n",
+    "\n",
+    "    trace = pm.sample()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9ca7b453",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_plot = np.linspace(-4, 8, num=1001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "19b4ca98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dirac = np.less.outer(x_plot, trace.posterior[\"atoms\"].values[0, 0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "9875702b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 20)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trace.posterior[\"sbw\"].values[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ee2fb857",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1001, 20)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dirac"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d00e46c2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pymc-dev",
+   "language": "python",
+   "name": "pymc-dev"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/dp-example-9.png b/notebooks/dp-example-9.png
new file mode 100644
index 00000000..a01da289
Binary files /dev/null and b/notebooks/dp-example-9.png differ
diff --git a/notebooks/dp-posterior-numpy.ipynb b/notebooks/dp-posterior-numpy.ipynb
new file mode 100644
index 00000000..18037f27
--- /dev/null
+++ b/notebooks/dp-posterior-numpy.ipynb
@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fcdf277f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.stats as st\n",
+    "\n",
+    "%config InlineBackend.figure_format = \"retina\"\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "980ba3a2",
+   "metadata": {},
+   "source": [
+    "I follow **Example 9** from [here](https://projecteuclid.org/ebooks/nsf-cbms-regional-conference-series-in-probability-and-statistics/Nonparametric-Bayesian-Inference/Chapter/Chapter-3-Dirichlet-Process/10.1214/cbms/1362163748) (displayed below) and attempt to also replicate the results shown in Figure 3.2. They assume that data are drawn i.i.d. from $\\mathcal{N}(2, 4)$ but assume a base distribution $G_0 = \\mathcal{N}(0, 1)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5299a263",
+   "metadata": {},
+   "source": [
+    "<img src=\"../progress/dp-example-9.png\" width=500>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4d8682af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 20\n",
+    "K = 25\n",
+    "M = 5\n",
+    "rng = np.random.RandomState(seed=34)\n",
+    "G0 = lambda: rng.normal(\n",
+    "    loc=0.0,\n",
+    "    scale=1.0,\n",
+    "    size=[\n",
+    "        1,\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def Gn(alpha, G0, obs):\n",
+    "    \"\"\"\n",
+    "    obs is a vector of observed data upon which we condition\n",
+    "    \"\"\"\n",
+    "\n",
+    "    n_obs = len(obs)\n",
+    "    idx = rng.binomial(\n",
+    "        n=1,\n",
+    "        p=alpha / (alpha + n_obs),\n",
+    "        size=[\n",
+    "            K + 1,\n",
+    "        ],\n",
+    "    )\n",
+    "\n",
+    "    posterior_draws = []\n",
+    "\n",
+    "    for i in range(K + 1):\n",
+    "        if idx[i] == 1:\n",
+    "            posterior_draws.append(float(G0()))\n",
+    "        elif idx[i] == 0:\n",
+    "            posterior_draws.append(rng.choice(obs))\n",
+    "        else:\n",
+    "            raise ValueError(\"Hmm something's not right\")\n",
+    "\n",
+    "    return np.array(posterior_draws)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "868a9a0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_plot = np.linspace(-4, 8, num=1001)\n",
+    "\n",
+    "\n",
+    "def cdf(alpha, obs):\n",
+    "\n",
+    "    global w\n",
+    "\n",
+    "    n_obs = len(obs)\n",
+    "    betas = np.random.beta(1, alpha + n_obs, size=[N, K])\n",
+    "    betas[:, 1:] = betas[:, 1:] * (1 - betas[:, :-1]).cumprod(axis=1)\n",
+    "\n",
+    "    w = betas.copy()  # weights\n",
+    "    w = np.hstack((w, (1 - w.sum(axis=1))[..., np.newaxis]))\n",
+    "    m = np.array([Gn(alpha, G0, obs) for _ in range(N)])\n",
+    "\n",
+    "    dirac = np.less.outer(m, x_plot).astype(int)\n",
+    "    return (w[..., np.newaxis] * dirac).sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6d42809a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 474,
+       "width": 1236
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
+    "dp_sample = {}\n",
+    "\n",
+    "# for i in range(N):\n",
+    "#     ax.plot(x_plot, dp_sample[i], c=\"k\", alpha=0.1)\n",
+    "\n",
+    "for fig_num in range(2):\n",
+    "\n",
+    "    if fig_num == 0:\n",
+    "        n_obs = 8\n",
+    "    elif fig_num == 1:\n",
+    "        n_obs = 50\n",
+    "\n",
+    "    x_obs = rng.normal(\n",
+    "        loc=2.0,\n",
+    "        scale=2.0,\n",
+    "        size=[\n",
+    "            n_obs,\n",
+    "        ],\n",
+    "    )\n",
+    "    dp_sample[n_obs] = cdf(5, x_obs)\n",
+    "\n",
+    "    _ = ax[fig_num].plot(\n",
+    "        x_plot,\n",
+    "        dp_sample[n_obs].mean(axis=0),\n",
+    "        c=\"k\",\n",
+    "        linestyle=\"-\",\n",
+    "        linewidth=2,\n",
+    "        label=\"posterior E(F|y)\",\n",
+    "    )\n",
+    "    _ = ax[fig_num].plot(x_plot, st.norm.cdf(x_plot), c=\"k\", linestyle=\"-.\", label=\"prior E(F)\")\n",
+    "    _ = ax[fig_num].plot(\n",
+    "        x_plot,\n",
+    "        np.greater.outer(x_plot, x_obs).mean(axis=1),\n",
+    "        c=\"k\",\n",
+    "        linestyle=\"dotted\",\n",
+    "        alpha=0.5,\n",
+    "        label=\"empirical\",\n",
+    "    )\n",
+    "    _ = ax[fig_num].plot(\n",
+    "        x_plot, st.norm.cdf(x_plot, loc=2.0, scale=2.0), c=\"k\", linestyle=\"--\", label=\"truth\"\n",
+    "    )\n",
+    "\n",
+    "    _ = ax[fig_num].set_title(f\"# of observations = {n_obs}\", fontsize=14)\n",
+    "    _ = ax[fig_num].set_ylabel(\"F(x)\", fontsize=12)\n",
+    "    _ = ax[fig_num].set_xlabel(\"x\", fontsize=12)\n",
+    "    _ = ax[fig_num].legend()\n",
+    "\n",
+    "plt.savefig(fname=\"dp-posterior\", dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f988a53e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 450,
+       "width": 1214
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
+    "\n",
+    "_ = ax[0].plot(x_plot, dp_sample[8][0], c=\"k\", linestyle=\"-\", linewidth=0.5)\n",
+    "_ = ax[1].plot(x_plot, dp_sample[50][0], c=\"k\", linestyle=\"-\", linewidth=0.5)\n",
+    "\n",
+    "for i in range(2):\n",
+    "    _ = ax[fig_num].set_ylabel(\"F(x)\", fontsize=12)\n",
+    "    _ = ax[fig_num].set_xlabel(\"x\", fontsize=12)\n",
+    "\n",
+    "_ = plt.savefig(fname=\"dp-posterior-single-sample\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4dd5f4b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 450,
+       "width": 1214
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
+    "\n",
+    "for i in range(N):\n",
+    "    _ = ax[0].plot(x_plot, dp_sample[8][i], c=\"k\", linestyle=\"-\", linewidth=0.5)\n",
+    "    _ = ax[1].plot(x_plot, dp_sample[50][i], c=\"k\", linestyle=\"-\", linewidth=0.5)\n",
+    "\n",
+    "for i in range(2):\n",
+    "    _ = ax[fig_num].set_ylabel(\"F(x)\", fontsize=12)\n",
+    "    _ = ax[fig_num].set_xlabel(\"x\", fontsize=12)\n",
+    "\n",
+    "_ = plt.savefig(fname=\"dp-posterior-multiple-samples\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5b3abd0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pymc-dev",
+   "language": "python",
+   "name": "pymc-dev"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pymc_experimental/__init__.py b/pymc_experimental/__init__.py
index b29ff330..a6c5c0dc 100644
--- a/pymc_experimental/__init__.py
+++ b/pymc_experimental/__init__.py
@@ -26,6 +26,6 @@
         _log.addHandler(handler)
 
 
-from pymc_experimental import distributions, gp, utils
+from pymc_experimental import distributions, dp, gp, utils
 from pymc_experimental.inference.fit import fit
 from pymc_experimental.marginal_model import MarginalModel
diff --git a/pymc_experimental/dp/__init__.py b/pymc_experimental/dp/__init__.py
new file mode 100644
index 00000000..62085863
--- /dev/null
+++ b/pymc_experimental/dp/__init__.py
@@ -0,0 +1,18 @@
+#   Copyright 2020 The PyMC Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+
+from pymc_experimental.dp.dp import DirichletProcess, DirichletProcessMixture
+
+__all__ = ["DirichletProcess", "DirichletProcessMixture"]
diff --git a/pymc_experimental/dp/dp.py b/pymc_experimental/dp/dp.py
new file mode 100644
index 00000000..50fc1876
--- /dev/null
+++ b/pymc_experimental/dp/dp.py
@@ -0,0 +1,119 @@
+#   Copyright 2020 The PyMC Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+import numpy as np
+import pymc as pm
+import pytensor.tensor as pt
+from pymc.distributions import Mixture
+from pymc.model import modelcontext
+
+__all__ = ["DirichletProcess", "DirichletProcessMixture"]
+
+
+def DirichletProcess(name, alpha, base_dist, K, observed=None, sbw_name=None, atoms_name=None):
+    r"""
+    Truncated Dirichlet Process for Bayesian Nonparametric Density Modelling
+
+    Parameters
+    ----------
+    alpha: tensor_like of float
+        Scale concentration parameter (alpha > 0) specifying the size of "sticks", or generated
+        weights, from the stick-breaking process. Ideally, alpha should have a prior and not be
+        a fixed constant.
+    base_dist: single batched distribution
+        The base distribution for a Dirichlet Process. `base_dist` must have shape (K + 1,).
+    K: int
+        The truncation parameter for the number of components of the Dirichlet Process Mixture.
+        The Goldilocks Principle should be used in selecting an appropriate value of K: not too
+        low to capture all possible clusters and not too high to induce a heavy computational
+        burden for sampling.
+    """
+    if sbw_name is None:
+        sbw_name = "sbw"
+
+    if atoms_name is None:
+        atoms_name = "atoms"
+
+    if observed is not None:
+        observed = np.asarray(observed)
+
+        if observed.ndim > 1:
+            raise ValueError("Multi-dimensional Dirichlet Processes are not " "yet supported.")
+
+        N = observed.shape[0]
+
+    try:
+        modelcontext(None)
+    except TypeError:
+        raise ValueError(
+            "PyMC Dirichlet Processes are only available under a pm.Model() context manager."
+        )
+
+    sbw = pm.StickBreakingWeights(sbw_name, alpha, K)
+
+    if observed is None:
+        return sbw, pm.Deterministic(atoms_name, base_dist)
+
+    """
+    idx samples a new atom from `base_dist` with probability alpha/(alpha + N)
+    and an existing atom from `observed` with probability N/(alpha + N).
+
+    If a new atom is not sampled, an atom from `observed` is sampled uniformly.
+    """
+    idx = pm.Bernoulli("idx", p=alpha / (alpha + N), shape=(K + 1,))
+    atom_selection = pm.Categorical("atom_selection", p=[1 / N] * N, shape=(K + 1,))
+
+    atoms = pm.Deterministic(
+        atoms_name,
+        var=pt.stack([pt.constant(observed)[atom_selection], base_dist], axis=-1)[
+            pt.arange(K + 1), idx
+        ],
+    )
+
+    return sbw, atoms
+
+
+class DirichletProcessMixture(Mixture):
+    r"""
+    Truncated Dirichlet Process Mixture
+
+    Parameters
+    ----------
+    alpha: tensor_like of float
+        Scale concentration parameter (alpha > 0) specifying the size of "sticks", or generated
+        weights, from the stick-breaking process. Ideally, alpha should have a prior and not be
+        a fixed constant.
+    G0: single batched distribution
+        The base distribution for a Dirichlet Process Mixture should be created via the
+        `.dist()` API as this class inherits from `pm.Mixture`. Be sure that the last size
+        of G0 is K+1.
+    K: int
+        The truncation parameter for the number of components of the Dirichlet Process Mixture.
+        The Goldilocks Principle should be used in selecting an appropriate value of K: not too
+        low to capture all possible clusters and not too high to induce a heavy computational
+        burden for sampling.
+    """
+
+    def __new__(cls, name, alpha, G0, K, **kwargs):
+        if "sbw_name" in kwargs:
+            sbw_name = kwargs["sbw_name"]
+        else:
+            sbw_name = f"sbw_{name}"
+
+        model = modelcontext(None)
+        model.register_rv(
+            pm.StickBreakingWeights.dist(alpha, K, **kwargs),
+            sbw_name,
+        )
+        return super().__new__(cls, name, w=model[sbw_name], comp_dists=G0, **kwargs)