diff --git a/conftest.py b/conftest.py
new file mode 100644
index 00000000..3178a8fd
--- /dev/null
+++ b/conftest.py
@@ -0,0 +1,19 @@
+import pytest
+
+
+def pytest_addoption(parser):
+    parser.addoption("--runslow", action="store_true", default=False, help="run slow tests")
+
+
+def pytest_configure(config):
+    config.addinivalue_line("markers", "slow: mark test as slow to run")
+
+
+def pytest_collection_modifyitems(config, items):
+    if config.getoption("--runslow"):
+        # --runslow given in cli: do not skip slow tests
+        return
+    skip_slow = pytest.mark.skip(reason="need --runslow option to run")
+    for item in items:
+        if "slow" in item.keywords:
+            item.add_marker(skip_slow)
diff --git a/notebooks/marginalized_changepoint_model.ipynb b/notebooks/marginalized_changepoint_model.ipynb
new file mode 100644
index 00000000..e0bd9cb1
--- /dev/null
+++ b/notebooks/marginalized_changepoint_model.ipynb
@@ -0,0 +1,821 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymc as pm\n",
+    "from pymc_experimental.marginal_model import MarginalModel\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import arviz as az"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The original model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Try to handle np.nan\n",
+    "# fmt: off\n",
+    "disaster_data = pd.Series(\n",
+    "    [4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,\n",
+    "     3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,\n",
+    "     2, 2, 3, 4, 2, 1, 3, np.nan, 2, 1, 1, 1, 1, 3, 0, 0,\n",
+    "     1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,\n",
+    "     0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,\n",
+    "     3, 3, 1, np.nan, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,\n",
+    "     0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1]\n",
+    ")\n",
+    "# fmt: on\n",
+    "\n",
+    "years = np.arange(1851, 1962)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ricardo/Documents/Projects/pymc/pymc/model.py:1378: ImputationWarning: Data in disasters contains missing values and will be automatically imputed from the sampling distribution.\n",
+      "  warnings.warn(impute_message, ImputationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "with MarginalModel() as disaster_model:\n",
+    "    switchpoint = pm.DiscreteUniform(\"switchpoint\", lower=years.min(), upper=years.max())\n",
+    "\n",
+    "    early_rate = pm.Exponential(\"early_rate\", 1.0)\n",
+    "    late_rate = pm.Exponential(\"late_rate\", 1.0)\n",
+    "    rate = pm.math.switch(switchpoint >= years, early_rate, late_rate)\n",
+    "\n",
+    "    disasters = pm.Poisson(\"disasters\", rate, observed=disaster_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
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+       "<title>disasters_observed</title>\n",
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+       "<!-- switchpoint&#45;&gt;disasters_observed -->\n",
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+       "<title>early_rate</title>\n",
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+       "<g id=\"edge3\" class=\"edge\">\n",
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+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x7f818259b0a0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm.model_to_graphviz(disaster_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">CompoundStep\n",
+      ">>Metropolis: [switchpoint]\n",
+      ">>Metropolis: [disasters_missing]\n",
+      ">NUTS: [early_rate, late_rate]\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",
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+      ],
+      "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 00:05&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 6 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with disaster_model:\n",
+    "    trace = pm.sample()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
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\n",
+      "text/plain": [
+       "<Figure size 2944x552 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(trace, var_names=[\"~switchpoint\", \"~disasters\"]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<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>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>disasters_missing[0]</th>\n",
+       "      <td>2.203</td>\n",
+       "      <td>1.869</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>5.000</td>\n",
+       "      <td>0.095</td>\n",
+       "      <td>0.067</td>\n",
+       "      <td>373.0</td>\n",
+       "      <td>417.0</td>\n",
+       "      <td>1.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disasters_missing[1]</th>\n",
+       "      <td>0.913</td>\n",
+       "      <td>0.967</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>3.000</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>723.0</td>\n",
+       "      <td>932.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>early_rate</th>\n",
+       "      <td>3.081</td>\n",
+       "      <td>0.289</td>\n",
+       "      <td>2.551</td>\n",
+       "      <td>3.646</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>2427.0</td>\n",
+       "      <td>2785.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>late_rate</th>\n",
+       "      <td>0.930</td>\n",
+       "      <td>0.116</td>\n",
+       "      <td>0.713</td>\n",
+       "      <td>1.149</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>2725.0</td>\n",
+       "      <td>2749.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "disasters_missing[0]  2.203  1.869   0.000    5.000      0.095    0.067   \n",
+       "disasters_missing[1]  0.913  0.967   0.000    3.000      0.037    0.027   \n",
+       "early_rate            3.081  0.289   2.551    3.646      0.006    0.004   \n",
+       "late_rate             0.930  0.116   0.713    1.149      0.002    0.002   \n",
+       "\n",
+       "                      ess_bulk  ess_tail  r_hat  \n",
+       "disasters_missing[0]     373.0     417.0   1.01  \n",
+       "disasters_missing[1]     723.0     932.0   1.00  \n",
+       "early_rate              2427.0    2785.0   1.00  \n",
+       "late_rate               2725.0    2749.0   1.00  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "az.summary(trace, var_names=[\"~switchpoint\", \"~disasters\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Marginalized model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ricardo/Documents/Projects/pymc-experimental/pymc_experimental/marginal_model.py:117: UserWarning: There are multiple dependent variables in a FiniteDiscreteMarginalRV.\n",
+      "Their joint logp terms will be assigned to the first RV: disasters_missing\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "disaster_model.marginalize([switchpoint])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
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+      ],
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+       "<graphviz.graphs.Digraph at 0x7f8158e2f880>"
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+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm.model_to_graphviz(disaster_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [early_rate, late_rate]\n",
+      ">Metropolis: [disasters_missing]\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",
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+       "</style>\n"
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+       "<IPython.core.display.HTML object>"
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+     "metadata": {},
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+    },
+    {
+     "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 01:16&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 77 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with disaster_model:\n",
+    "    trace = pm.sample()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
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VlTk1M36bsx7T6tfEX49oy8nJ0YoVKxQXF8dnB26G10vvwWfB7qFJHp29e/dKUq1z/OLi4hQaGlpzGtTt0KFDWrBggVq2bKlevXpZHcfllJaW6tprr1WfPn30wAMPWB3Hpa1fv16S5OPjo969e2vPnj01+2w2m+655x699NJLVsVzOc2aNdPzzz+v++67T127dtWUKVMUHh6uzZs3a9GiRbrtttt0xx13WB3Tpf3W60D19nnz5mnv3r1q27ZtU0ZzO++++66k2t9oAb+FegzOQk36P2VlZXr22WdlmqaysrK0cOFC7dq1S9dff73XNwVTm9ft6NGjuv7660/YNnDgQH388cdKTEy0KJW1eH+CutTnNaewsFBpaWnq2bNnrSs7VNdA1Dunp6F1wMyZMzVz5sya//v6+urOO+/Uiy++yIpw9XQ69QfPk8bhrJqQ54lznG4dyvMEOD3O+PyprKxMW7duVUxMjF5//XU99thjcjgcNfsTEhI0e/Zsr/9Moqk46zPFv/zlL/r22291zz336IcfflDv3r2Vl5en2bNnKzg4WF999ZWCgoKcnh+Ng9dL78JxKffQJI0/ubm5knTSiJxq4eHhNadB7crLy3XNNdeotLRU//jHP3izV4tHH31Ue/fu1fr167l/fkd6erok6eWXX1a/fv20du1adevWTRs3btQtt9yif/7zn0pMTNSf/vQni5O6jnvuuUetW7fWTTfdpDfffLNm+/Dhw3XllVfS5fo7TuV14JenQ+3eeustzZ07V2PHjj1pSVTg91CPwRmoSU9UVlamJ554oub/hmHo/vvv13PPPWdhKtdAbV6766+/XiNGjFDPnj0VGhqqPXv26OWXX9YHH3ygcePGaevWrQoLC7M6ZpPj/QlqU9/XHN5zNL6G1AExMTF6/vnnNXnyZMXHx6uwsFCrVq3SQw89pH/9618yDEP//Oc/myC95zid+oPnSeM43ZqQ54lznW4dyvMEOD3O+Pzp+PHjstvtysrK0pNPPqkXXnhB11xzjcrLy/Wf//xHTz/9tM4991zt2rVLgYGBTr8NOJGzPlNs0aKFVq1apauvvlpz586tWR0vKChIf/zjH3XGGWc4LzQaHa+X3mPTpk164oknFBsby5f7XFyTjPrC6XE4HJo6daqWLl2qm2++Wddcc43VkVzOqlWr9NJLL+nhhx9mrMIpqO6O9/f31+zZszVw4ECFhoZqxIgR+vzzz2Wz2fhA4VeefPJJXX311frb3/6mlJQU5efna9myZSopKdHo0aP1zTffWB0RHu67777THXfcofbt2+vDDz+0Og4AL0RNerLQ0FCZpim73a6UlBRNmzZN77zzjkaPHn3SeFBvQm1et8cee0xjx45VbGysgoOD1adPH73//vu65pprlJycrLffftvqiJbg/Ql+jdcc19PQx6RHjx568MEH1aNHD4WEhCg2NlZTpkzR4sWLFRMTo9dee62m+Q+nhvrD9ZzuY8LzxHmoQwHPUP3+wG6367bbbtN9992n2NhYtW7dWk8++aQuueQSJScn64svvrA4Kepj3759GjZsmDIyMrRs2TLl5+crJSVFjz76qJ566imNGzdOdrvd6pgAfiEpKUmTJk2S3W7XJ598oubNm1sdCb+hSRp/qrv96urqy8vLq7Mj0Ns5HA7dcMMNmjlzpq6++uoTVhpBpYqKCl133XXq3bu3HnroIavjuIXq59uAAQPUqlWrE/b17NlTCQkJ2r9/v3JycixI53oWLFigxx57THfccYceeughtWnTRqGhoRo+fLi+/fZb+fn56b777rM6pks7ldeBX54OJ5ozZ44uvvhitWjRQosWLVLLli2tjgQ3RD2G00FN+ttsNpvatGmjP/3pT3rrrbe0YsUKPfPMM1bHsgS1ecPceuutkqQVK1ZYnMQavD/BLzX0NYf3HI2nMeqAuLg4TZkyRRUVFVqzZo0TUnqfhtQfPE8al7NrQp4n9eOsOpTnCXB6nPH50y/3n3feeSftr962bt26hsZEPTjrM8WpU6cqOTlZ3377rYYPH67Q0FC1adNGDz30kO68806tWrVKn3zyiVOzo/Hweun5Dhw4oDFjxigzM1NffPGFxowZY3Uk/I4mafz5rTl+R48eVUFBQa2zIb2dw+HQ9ddfrxkzZuiKK67Q9OnTZbOxSNOvFRQUaO/evdq0aZP8/f1lGEbNnxkzZkiShg4dKsMwNHv2bGvDuoguXbpIkiIjI2vdX729uLi4iRK5trlz50pSrS9qcXFx6tq1q/bt26eCgoKmjuY2fm+e62/NCfZ233//vS688EI1b95cixcvVkJCgtWR4Kaox9BQ1KT1Uz3resmSJdYGsQi1ecNUf2OqsLDQ4iTW4P0Jqp3Oa05ISIhatmypAwcO1PpNYd5zNExj1gHe/rvPmU61/uB50nScVRPyPDl1zqpDeZ4Ap8cZnz+FhISodevWkmp/j8D7g6bljMc0Pz9fK1asULdu3RQXF3fS/upjLxs3bnRCYjQFXi89W1JSkkaPHq20tDR99tlnmjx5stWRcAqa5BP7UaNGSZLmz59/0r558+adcBpUqv5g5f3339dll12mDz74oEEzib1BQECAbrzxxlr/VL+gnHfeebrxxhsVHx9vbVgXUV1E7dy586R95eXl2rdvn0JCQhQTE9PU0VxSWVmZJCkjI6PW/RkZGbLZbPLz82vKWG6lU6dOatWqlVasWHHSh1WFhYVasWKFOnTooLZt21qU0DV9//33uuiiixQdHa3FixerY8eOVkeCG6MeQ0NQk9bfkSNHJMlr6wJq84ap/ha/t94nvD+B5JzXnFGjRtW8v/i16npn5MiRTsnrDRq7DvD2333OVJ/6g+dJ03BWTcjz5NQ5sw7leQI0nLM+fxo7dqwkaceOHSftq97G78am4YzHtPoYS2ZmZq37q4+9BAQENDgnmh6vl54pKSlJY8aMUVpamj799FNNmTLF6kg4VWYTKC8vNxMSEsyAgABz48aNNdtzcnLMzp07m/7+/uaBAweaIopbsNvt5nXXXWdKMi+55BKzvLzc6khuq/p+XLVqldVRXM6ECRNMSebbb799wvYnn3zSlGReffXVFiVzPR9//LEpyezRo4eZk5Nzwr433njDlGQOGzbMonSu47nnnjMlme+9916t+x999FFTkvnggw+esP3BBx80JZnPPvtsE6R0Hb93f82ZM8cMCAgw4+LizF27djVtOHgk6rFT83vPTW9CTVq37du3m4WFhSdtLywsNM8++2xTkvnMM89YkMy1eXttvnPnzlp/bnbu3GnGxcWZksyffvrJgmSugfcn3q2+rzkZGRnmzp07zYyMjBO2L1q0yJRkjhw50iwtLa3ZPmfOHFOSOWHChEbJ74mc9ZisW7eu1tO/8sorpiSzU6dOZkVFhdNye7L61h88Txqfsx4TnieNr646lOcJ4Hz1/fzpyJEj5s6dO0/63H3FihU1n8lnZ2fXbE9LSzNbt25t2mw2c/fu3Y18a2CazntMu3TpUut7vuzsbLNr166mJPPHH39szJuCOvze56G8Xrqfhj6mSUlJZrt27UxfX1/zyy+/bIKkcCbDNE2zUTuLqixevFgTJ05UYGCgLr/8coWFhenLL79UcnKyXnrpJd13331NEcMtPP7443riiScUGhqqu+66S76+vied5vzzz1efPn2aPpybmTp1qmbMmKFVq1ZpyJAhVsdxKfv379eZZ56p9PR0TZo0SV27dtXGjRu1aNEitW/fXqtXr651yUVvZLfbNfb/27t70Ki2LQDAi8cMYZIQAgGJP6PTKMI0RpIgZFSioIIYC/GnEExhKptAqlRJFa0CYiUE0lqIoIVhLDSK4B9ERBARGxULFVEEQYmyX/GI3Hs1V+eRzDjO98F0h5m1zz777HU26+zZsSNu3LgRK1asiIGBgWhvb4+5ubm4evVq5HK5mJ2djd7e3lqHWnVTU1Nx8+bNiIh4+PBhzM3NRV9f37edaUqlUhw/fjwi/rezT19fXzx48CB27doVmzdvjrm5ubhy5Ur09PTE9evXI5fL1awt1fCr5+vx48exadOm+Pz5cxw5cuTb31/8VaFQiMHBwWqGzx9APvZjldzLGomcdHHj4+MxOTkZpVIpCoVCtLW1xcuXL2NmZibevn0bW7dujXK5/MfPa5Vq9Nx84brZtm1brFu3LlpaWuLJkydx+fLlmJ+fj9HR0ZiYmKh1mDXj+aSxVTrnLBw/NjYW4+PjfztuaGgopqamolgsxt69e7+9pdja2hq3bt2KDRs2VKFF9W+p+qRQKEQ2m43u7u5Ys2ZNfPz4MW7fvh3379+P9vb2KJfLDfks/f+oNP8wTpbfUvWJcbL8FstDjRNYHpWsPy2Mz+np6e/WOkdGRmJycjLy+Xzs27cv5ufn4+LFi/H69euYmJiI0dHRKrescS1Fn87MzMTAwEB8+fIldu7cGV1dXfHu3bu4dOlSvHnzJg4cOBDnz5+vQesaUyXroebL+rAUfVooFOLZs2exZcuW2L179w9/55/XAL+RalYZ3blzJ+3Zsye1tbWlXC6Xent707lz56oZQl1YeAPh3z7eQv81jf5W8c88f/48DQ4Ops7OzpTNZlM+n08nTpxIr169qnVov51Pnz6lkydPpq6urtTc3JwymUxavXp1Onr0aHr06FGtw6uZn92vjh079rfj379/n4aHh1M+n0/ZbDatXbs2jYyMpA8fPtSmAVX2q+fr2rVrP50Htm/fXtO2UL/kY9+r9F7WKOSki7t3714aGhpKxWIxtbe3p0wmkzo6OlJ/f386e/as3ZEW0ei5+ezsbDp06FBav359amtrS5lMJnV2dqb9+/encrlc6/B+C55PGlelc87Y2FiKiDQ2Nvbdd339+jWdPn06FYvF1NTUlDo6OtLhw4fT06dPq9egP8BS9cmpU6dSf39/WrVqVWpqakq5XC5t3LgxDQ8PpxcvXlS3UXWu0vzDOFl+S9UnxsnyWywPNU5g+fzq+tPC+FxsfWF6ejp1d3en5ubm1NLSkkqlUrpw4cIyR8+PLEWf3r17Nx08eDCtXLkyZTKZ1Nramnp6etKZM2fsbldllayHmi/rw1L06c+eAatcWkKFqrbjDwAAAAAAAAAAsHT+U+sAAAAAAAAAAACAyin8AQAAAAAAAACAOqTwBwAAAAAAAAAA6pDCHwAAAAAAAAAAqEMKfwAAAAAAAAAAoA4p/AEAAAAAAAAAgDqk8AcAAAAAAAAAAOqQwh8AAAAAAAAAAKhDCn8AAAAAAAAAAKAOKfwBAAAAAAAAAIA6pPAHAAAAAAAAAADqkMIfAAAAAAAAAACoQwp/AAAAAAAAAACgDv0XkdSf+M3UJSIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 2944x552 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(trace, var_names=\"~disasters\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<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>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>disasters_missing[0]</th>\n",
+       "      <td>2.272</td>\n",
+       "      <td>1.906</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>6.000</td>\n",
+       "      <td>0.079</td>\n",
+       "      <td>0.060</td>\n",
+       "      <td>618.0</td>\n",
+       "      <td>685.0</td>\n",
+       "      <td>1.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disasters_missing[1]</th>\n",
+       "      <td>0.990</td>\n",
+       "      <td>1.045</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>3.000</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>712.0</td>\n",
+       "      <td>652.0</td>\n",
+       "      <td>1.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>early_rate</th>\n",
+       "      <td>3.085</td>\n",
+       "      <td>0.283</td>\n",
+       "      <td>2.538</td>\n",
+       "      <td>3.592</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>3770.0</td>\n",
+       "      <td>3235.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>late_rate</th>\n",
+       "      <td>0.929</td>\n",
+       "      <td>0.118</td>\n",
+       "      <td>0.711</td>\n",
+       "      <td>1.147</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>3214.0</td>\n",
+       "      <td>2258.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "disasters_missing[0]  2.272  1.906   0.000    6.000      0.079    0.060   \n",
+       "disasters_missing[1]  0.990  1.045   0.000    3.000      0.040    0.030   \n",
+       "early_rate            3.085  0.283   2.538    3.592      0.005    0.003   \n",
+       "late_rate             0.929  0.118   0.711    1.147      0.002    0.001   \n",
+       "\n",
+       "                      ess_bulk  ess_tail  r_hat  \n",
+       "disasters_missing[0]     618.0     685.0   1.01  \n",
+       "disasters_missing[1]     712.0     652.0   1.01  \n",
+       "early_rate              3770.0    3235.0   1.00  \n",
+       "late_rate               3214.0    2258.0   1.00  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "az.summary(trace, var_names=\"~disasters\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "pymcx",
+   "language": "python",
+   "name": "pymcx"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pymc_experimental/__init__.py b/pymc_experimental/__init__.py
index 838149d4..ed021a51 100644
--- a/pymc_experimental/__init__.py
+++ b/pymc_experimental/__init__.py
@@ -28,3 +28,4 @@
 
 from pymc_experimental import distributions, gp, utils
 from pymc_experimental.inference.fit import fit
+from pymc_experimental.marginal_model import MarginalModel
diff --git a/pymc_experimental/marginal_model.py b/pymc_experimental/marginal_model.py
new file mode 100644
index 00000000..0d94dcc9
--- /dev/null
+++ b/pymc_experimental/marginal_model.py
@@ -0,0 +1,477 @@
+import warnings
+from typing import Sequence, Tuple, Union
+
+import aesara.tensor as at
+import numpy as np
+from aeppl import factorized_joint_logprob
+from aeppl.abstract import _get_measurable_outputs
+from aeppl.logprob import _logprob
+from aesara import Mode
+from aesara.compile import SharedVariable
+from aesara.compile.builders import OpFromGraph
+from aesara.graph import Constant, FunctionGraph, ancestors, clone_replace
+from aesara.scan import map as scan_map
+from aesara.tensor import TensorVariable
+from aesara.tensor.elemwise import Elemwise
+from pymc import SymbolicRandomVariable
+from pymc.aesaraf import constant_fold, inputvars
+from pymc.distributions.discrete import Bernoulli, Categorical, DiscreteUniform
+from pymc.model import Model
+
+__all__ = ["MarginalModel"]
+
+
+class MarginalModel(Model):
+    """Subclass of PyMC Model that implements functionality for automatic
+    marginalization of variables in the logp transformation
+
+    After defining the full Model, the `marginalize` method can be used to indicate a
+    subset of variables that should be marginalized
+
+    Notes
+    -----
+    Marginalization functionality is still very restricted. Only finite discrete
+    variables can be marginalized. Deterministics and Potentials cannot be conditionally
+    dependent on the marginalized variables.
+
+    Furthermore, not all instances of such variables can be marginalized. If a variable
+    has batched dimensions, it is required that any conditionally dependent variables
+    use information from an individual batched dimension. In other words, the graph
+    connecting the marginalized variable(s) to the dependent variable(s) must be
+    composed strictly of Elemwise Operations. This is necessary to ensure an efficient
+    logprob graph can be generated. If you want to bypass this restriction you can
+    separate each dimension of the marginalized variable into the scalar components
+    and then stack them together. Note that such graphs will grow exponentially in the
+    number of  marginalized variables.
+
+    For the same reason, it's not possible to marginalize RVs with multivariate
+    dependent RVs.
+
+    Examples
+    --------
+
+    Marginalize over a single variable
+
+    .. code-block:: python
+        import pymc as pm
+        from pymc_experimental import MarginalModel
+
+        with MarginalModel() as m:
+            p = pm.Beta("p", 1, 1)
+            x = pm.Bernoulli("x", p=p, shape=(3,))
+            y = pm.Normal("y", pm.math.switch(x, -10, 10), observed=[10, 10, -10])
+
+            m.marginalize([x])
+
+            idata = pm.sample()
+
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.marginalized_rvs = []
+
+    def _delete_rv_mappings(self, rv: TensorVariable) -> None:
+        """Remove all model mappings referring to rv
+
+        This can be used to "delete" an RV from a model
+        """
+        assert rv in self.basic_RVs, "rv is not part of the Model"
+
+        name = rv.name
+        self.named_vars.pop(name)
+        if name in self.named_vars_to_dims:
+            self.named_vars_to_dims.pop(name)
+
+        value = self.rvs_to_values.pop(rv)
+        self.values_to_rvs.pop(value)
+
+        self.rvs_to_transforms.pop(rv)
+        if rv in self.free_RVs:
+            self.free_RVs.remove(rv)
+            self.rvs_to_initial_values.pop(rv)
+        else:
+            self.observed_RVs.remove(rv)
+        if rv in self.rvs_to_total_sizes:
+            self.rvs_to_total_sizes.pop(rv)
+
+    def _transfer_rv_mappings(self, old_rv: TensorVariable, new_rv: TensorVariable) -> None:
+        """Transfer model mappings from old_rv to new_rv"""
+
+        assert old_rv in self.basic_RVs, "old_rv is not part of the Model"
+        assert new_rv not in self.basic_RVs, "new_rv is already part of the Model"
+
+        self.named_vars.pop(old_rv.name)
+        new_rv.name = old_rv.name
+        self.named_vars[new_rv.name] = new_rv
+        if old_rv in self.named_vars_to_dims:
+            self._RV_dims[new_rv] = self._RV_dims.pop(old_rv)
+
+        value = self.rvs_to_values.pop(old_rv)
+        self.rvs_to_values[new_rv] = value
+        self.values_to_rvs[value] = new_rv
+
+        self.rvs_to_transforms[new_rv] = self.rvs_to_transforms.pop(old_rv)
+        if old_rv in self.free_RVs:
+            index = self.free_RVs.index(old_rv)
+            self.free_RVs.pop(index)
+            self.free_RVs.insert(index, new_rv)
+            self.rvs_to_initial_values[new_rv] = self.rvs_to_initial_values.pop(old_rv)
+        elif old_rv in self.observed_RVs:
+            index = self.observed_RVs.index(old_rv)
+            self.observed_RVs.pop(index)
+            self.observed_RVs.insert(index, new_rv)
+            if old_rv in self.rvs_to_total_sizes:
+                self.rvs_to_total_sizes[new_rv] = self.rvs_to_total_sizes.pop(old_rv)
+
+    def _marginalize(self, user_warnings=False):
+        fg = FunctionGraph(outputs=self.basic_RVs + self.marginalized_rvs, clone=False)
+
+        toposort = fg.toposort()
+        rvs_left_to_marginalize = self.marginalized_rvs
+        for rv_to_marginalize in sorted(
+            self.marginalized_rvs,
+            key=lambda rv: toposort.index(rv.owner),
+            reverse=True,
+        ):
+            # Check that no deterministics or potentials dependend on the rv to marginalize
+            for det in self.deterministics:
+                if is_conditional_dependent(
+                    det, rv_to_marginalize, self.basic_RVs + rvs_left_to_marginalize
+                ):
+                    raise NotImplementedError(
+                        f"Cannot marginalize {rv_to_marginalize} due to dependent Deterministic {det}"
+                    )
+            for pot in self.potentials:
+                if is_conditional_dependent(
+                    pot, rv_to_marginalize, self.basic_RVs + rvs_left_to_marginalize
+                ):
+                    raise NotImplementedError(
+                        f"Cannot marginalize {rv_to_marginalize} due to dependent Potential {pot}"
+                    )
+
+            old_rvs, new_rvs = replace_finite_discrete_marginal_subgraph(
+                fg, rv_to_marginalize, self.basic_RVs + rvs_left_to_marginalize
+            )
+
+            if user_warnings and len(new_rvs) > 2:
+                warnings.warn(
+                    "There are multiple dependent variables in a FiniteDiscreteMarginalRV. "
+                    f"Their joint logp terms will be assigned to the first RV: {old_rvs[1]}",
+                    UserWarning,
+                )
+
+            rvs_left_to_marginalize.remove(rv_to_marginalize)
+
+            for old_rv, new_rv in zip(old_rvs, new_rvs):
+                new_rv.name = old_rv.name
+                if old_rv in self.marginalized_rvs:
+                    idx = self.marginalized_rvs.index(old_rv)
+                    self.marginalized_rvs.pop(idx)
+                    self.marginalized_rvs.insert(idx, new_rv)
+                if old_rv in self.basic_RVs:
+                    self._transfer_rv_mappings(old_rv, new_rv)
+                    if user_warnings:
+                        transform = self.rvs_to_transforms[new_rv]
+                        if transform is not None:
+                            warnings.warn(
+                                "Transforms for variables that depend on marginalized RVs are currently not working, "
+                                f"rv={new_rv}, transform={transform}",
+                                UserWarning,
+                            )
+        return self
+
+    def _logp(self, *args, **kwargs):
+        return super().logp(*args, **kwargs)
+
+    def logp(self, vars=None, **kwargs):
+        m = self.clone()._marginalize()
+        if vars is not None:
+            if not isinstance(vars, Sequence):
+                vars = (vars,)
+            vars = [m[var.name] for var in vars]
+        return m._logp(vars=vars, **kwargs)
+
+    def clone(self):
+        m = MarginalModel()
+        vars = self.basic_RVs + self.potentials + self.deterministics + self.marginalized_rvs
+        cloned_vars = clone_replace(vars)
+        vars_to_clone = {var: cloned_var for var, cloned_var in zip(vars, cloned_vars)}
+
+        m.named_vars = {name: vars_to_clone[var] for name, var in self.named_vars.items()}
+        m.named_vars_to_dims = self.named_vars_to_dims
+        m.values_to_rvs = {i: vars_to_clone[rv] for i, rv in self.values_to_rvs.items()}
+        m.rvs_to_values = {vars_to_clone[rv]: i for rv, i in self.rvs_to_values.items()}
+        m.rvs_to_transforms = {vars_to_clone[rv]: i for rv, i in self.rvs_to_transforms.items()}
+        # Special logic due to bug in pm.Model
+        m.rvs_to_total_sizes = {
+            vars_to_clone[rv]: i for rv, i in self.rvs_to_total_sizes.items() if rv in vars_to_clone
+        }
+        m.rvs_to_initial_values = {
+            vars_to_clone[rv]: i for rv, i in self.rvs_to_initial_values.items()
+        }
+        m.free_RVs = [vars_to_clone[rv] for rv in self.free_RVs]
+        m.observed_RVs = [vars_to_clone[rv] for rv in self.observed_RVs]
+        m.potentials = [vars_to_clone[pot] for pot in self.potentials]
+        m.deterministics = [vars_to_clone[det] for det in self.deterministics]
+
+        m.marginalized_rvs = [vars_to_clone[rv] for rv in self.marginalized_rvs]
+        return m
+
+    def marginalize(self, rvs_to_marginalize: Union[TensorVariable, Sequence[TensorVariable]]):
+        if not isinstance(rvs_to_marginalize, Sequence):
+            rvs_to_marginalize = (rvs_to_marginalize,)
+
+        supported_dists = (Bernoulli, Categorical, DiscreteUniform)
+        for rv_to_marginalize in rvs_to_marginalize:
+            if rv_to_marginalize not in self.free_RVs:
+                raise ValueError(
+                    f"Marginalized RV {rv_to_marginalize} is not a free RV in the model"
+                )
+            if not isinstance(rv_to_marginalize.owner.op, supported_dists):
+                raise NotImplementedError(
+                    f"RV with distribution {rv_to_marginalize.owner.op} cannot be marginalized. "
+                    f"Supported distribution include {supported_dists}"
+                )
+
+            self._delete_rv_mappings(rv_to_marginalize)
+            self.marginalized_rvs.append(rv_to_marginalize)
+
+        # Raise errors and warnings immediately
+        self.clone()._marginalize(user_warnings=True)
+
+
+class MarginalRV(SymbolicRandomVariable):
+    """Base class for Marginalized RVs"""
+
+
+class FiniteDiscreteMarginalRV(MarginalRV):
+    """Base class for Finite Discrete Marginalized RVs"""
+
+
+def find_conditional_input_rvs(output_rvs, all_rvs):
+    """Find conditionally indepedent input RVs"""
+    blockers = [other_rv for other_rv in all_rvs if other_rv not in output_rvs]
+    return [
+        var
+        for var in ancestors(output_rvs, blockers=blockers)
+        if var in blockers
+        or (var.owner is None and not isinstance(var, (Constant, SharedVariable)))
+    ]
+
+
+def is_conditional_dependent(
+    dependent_rv: TensorVariable, dependable_rv: TensorVariable, all_rvs
+) -> bool:
+    """Check if dependent_rv is conditionall dependent on dependable_rv,
+    given all conditionally independent all_rvs"""
+
+    return dependable_rv in find_conditional_input_rvs((dependent_rv,), all_rvs)
+
+
+def find_conditional_dependent_rvs(dependable_rv, all_rvs):
+    """Find rvs than depend on dependable"""
+    return [
+        rv
+        for rv in all_rvs
+        if (rv is not dependable_rv and is_conditional_dependent(rv, dependable_rv, all_rvs))
+    ]
+
+
+def is_elemwise_subgraph(rv_to_marginalize, other_input_rvs, output_rvs):
+    # TODO: No need to consider apply nodes outside the subgraph...
+    fg = FunctionGraph(outputs=output_rvs, clone=False)
+
+    non_elemwise_blockers = [
+        o for node in fg.apply_nodes if not isinstance(node.op, Elemwise) for o in node.outputs
+    ]
+    blocker_candidates = [rv_to_marginalize] + other_input_rvs + non_elemwise_blockers
+    blockers = [var for var in blocker_candidates if var not in output_rvs]
+
+    truncated_inputs = [
+        var
+        for var in ancestors(output_rvs, blockers=blockers)
+        if (
+            var in blockers
+            or (var.owner is None and not isinstance(var, (Constant, SharedVariable)))
+        )
+    ]
+
+    # Check that we reach the marginalized rv following a pure elemwise graph
+    if rv_to_marginalize not in truncated_inputs:
+        return False
+
+    # Check that none of the truncated inputs depends on the marginalized_rv
+    other_truncated_inputs = [inp for inp in truncated_inputs if inp is not rv_to_marginalize]
+    # TODO: We don't need to go all the way to the root variables
+    if rv_to_marginalize in ancestors(
+        other_truncated_inputs, blockers=[rv_to_marginalize, *other_input_rvs]
+    ):
+        return False
+    return True
+
+
+def replace_finite_discrete_marginal_subgraph(fgraph, rv_to_marginalize, all_rvs):
+    # TODO: This should eventually be integrated in a more general routine that can
+    #  identify other types of supported marginalization, of which finite discrete
+    #  RVs is just one
+
+    dependent_rvs = find_conditional_dependent_rvs(rv_to_marginalize, all_rvs)
+    if not dependent_rvs:
+        raise ValueError(f"No RVs depend on marginalized RV {rv_to_marginalize}")
+
+    ndim_supp = {rv.owner.op.ndim_supp for rv in dependent_rvs}
+    if max(ndim_supp) > 0:
+        raise NotImplementedError(
+            "Marginalization of withe dependent Multivariate RVs not implemented"
+        )
+
+    marginalized_rv_input_rvs = find_conditional_input_rvs([rv_to_marginalize], all_rvs)
+    dependent_rvs_input_rvs = [
+        rv
+        for rv in find_conditional_input_rvs(dependent_rvs, all_rvs)
+        if rv is not rv_to_marginalize
+    ]
+
+    # If the marginalized RV has batched dimensions, check that graph between
+    # marginalized RV and dependent RVs is composed strictly of Elemwise Operations.
+    # This implies (?) that the dimensions are completely independent and a logp graph
+    # can ultimately be generated that is proportional to the support domain and not
+    # to the variables dimensions
+    # We don't need to worry about this if the  RV is scalar.
+    if np.prod(constant_fold(tuple(rv_to_marginalize.shape))) > 1:
+        if not is_elemwise_subgraph(rv_to_marginalize, dependent_rvs_input_rvs, dependent_rvs):
+            raise NotImplementedError(
+                "The subgraph between a marginalized RV and its dependents includes non Elemwise operations. "
+                "This is currently not supported",
+            )
+
+    input_rvs = [*marginalized_rv_input_rvs, *dependent_rvs_input_rvs]
+    rvs_to_marginalize = [rv_to_marginalize, *dependent_rvs]
+
+    outputs = rvs_to_marginalize
+    # Clone replace inner RV rng inputs so that we can be sure of the update order
+    # replace_inputs = {rng: rng.type() for rng in updates_rvs_to_marginalize.keys()}
+    # Clone replace outter RV inputs, so that their shared RNGs don't make it into
+    # the inner graph of the marginalized RVs
+    # FIXME: This shouldn't be needed!
+    replace_inputs = {}
+    replace_inputs.update({input_rv: input_rv.type() for input_rv in input_rvs})
+    cloned_outputs = clone_replace(outputs, replace=replace_inputs)
+
+    marginalization_op = FiniteDiscreteMarginalRV(
+        inputs=list(replace_inputs.values()),
+        outputs=cloned_outputs,
+        ndim_supp=0,
+    )
+    marginalized_rvs = marginalization_op(*replace_inputs.keys())
+    fgraph.replace_all(tuple(zip(rvs_to_marginalize, marginalized_rvs)))
+    return rvs_to_marginalize, marginalized_rvs
+
+
+@_get_measurable_outputs.register(FiniteDiscreteMarginalRV)
+def _get_measurable_outputs_finite_discrete_marginal_rv(op, node):
+    # Marginalized RVs are not measurable
+    return node.outputs[1:]
+
+
+def get_domain_of_finite_discrete_rv(rv: TensorVariable) -> Tuple[int, ...]:
+    op = rv.owner.op
+    if isinstance(op, Bernoulli):
+        return (0, 1)
+    elif isinstance(op, Categorical):
+        p_param = rv.owner.inputs[3]
+        return tuple(range(at.get_vector_length(p_param)))
+    elif isinstance(op, DiscreteUniform):
+        lower, upper = constant_fold(rv.owner.inputs[3:])
+        return tuple(range(lower, upper + 1))
+
+    raise NotImplementedError(f"Cannot compute domain for op {op}")
+
+
+@_logprob.register(FiniteDiscreteMarginalRV)
+def finite_discrete_marginal_rv_logp(op, values, *inputs, **kwargs):
+    # Clone the inner RV graph of the Marginalized RV
+    marginalized_rvs_node = op.make_node(*inputs)
+    marginalized_rv, *dependent_rvs = clone_replace(
+        op.inner_outputs,
+        replace={u: v for u, v in zip(op.inner_inputs, marginalized_rvs_node.inputs)},
+    )
+
+    # Obtain the joint_logp graph of the inner RV graph
+    # Some inputs are not root inputs (such as transformed projections of value variables)
+    # Or cannot be used as inputs to an OpFromGraph (shared variables and constants)
+    inputs = list(inputvars(inputs))
+    rvs_to_values = {}
+    dummy_marginalized_value = marginalized_rv.clone()
+    rvs_to_values[marginalized_rv] = dummy_marginalized_value
+    rvs_to_values.update(zip(dependent_rvs, values))
+    logps_dict = factorized_joint_logprob(rv_values=rvs_to_values, **kwargs)
+
+    # Reduce logp dimensions corresponding to broadcasted variables
+    values_axis_bcast = []
+    for value in values:
+        vbcast = value.type.broadcastable
+        mbcast = dummy_marginalized_value.type.broadcastable
+        mbcast = (True,) * (len(vbcast) - len(mbcast)) + mbcast
+        values_axis_bcast.append([i for i, (m, v) in enumerate(zip(mbcast, vbcast)) if m != v])
+    joint_logp = logps_dict[dummy_marginalized_value]
+    for value, values_axis_bcast in zip(values, values_axis_bcast):
+        joint_logp += logps_dict[value].sum(values_axis_bcast, keepdims=True)
+
+    # Wrap the joint_logp graph in an OpFromGrah, so that we can evaluate it at different
+    # values of the marginalized RV
+    # OpFromGraph does not accept constant inputs
+    non_const_values = [
+        value
+        for value in rvs_to_values.values()
+        if not isinstance(value, (Constant, SharedVariable))
+    ]
+    joint_logp_op = OpFromGraph([*non_const_values, *inputs], [joint_logp], inline=True)
+
+    # Compute the joint_logp for all possible n values of the marginalized RV. We assume
+    # each original dimension is independent so that it sufficies to evaluate the graph
+    # n times, once with each possible value of the marginalized RV replicated across
+    # batched dimensions of the marginalized RV
+
+    # PyMC does not allow RVs in the logp graph, even if we are just using the shape
+    marginalized_rv_shape = constant_fold(tuple(marginalized_rv.shape))
+    marginalized_rv_domain = get_domain_of_finite_discrete_rv(marginalized_rv)
+    marginalized_rv_domain_tensor = at.swapaxes(
+        at.full(
+            (*marginalized_rv_shape, len(marginalized_rv_domain)),
+            marginalized_rv_domain,
+            dtype=marginalized_rv.dtype,
+        ),
+        axis1=0,
+        axis2=-1,
+    )
+
+    # OpFromGraph does not accept constant inputs
+    non_const_values = [
+        value for value in values if not isinstance(value, (Constant, SharedVariable))
+    ]
+    # Arbitrary cutoff to switch to Scan implementation to keep graph size under control
+    if len(marginalized_rv_domain) <= 10:
+        joint_logps = [
+            joint_logp_op(marginalized_rv_domain_tensor[i], *non_const_values, *inputs)
+            for i in range(len(marginalized_rv_domain))
+        ]
+    else:
+        # Make sure this is rewrite is registered
+        from pymc.aesaraf import local_remove_check_parameter
+
+        def logp_fn(marginalized_rv_const, *non_sequences):
+            return joint_logp_op(marginalized_rv_const, *non_sequences)
+
+        joint_logps, _ = scan_map(
+            fn=logp_fn,
+            sequences=marginalized_rv_domain_tensor,
+            non_sequences=[*non_const_values, *inputs],
+            mode=Mode().including("local_remove_check_parameter"),
+        )
+
+    joint_logps = at.logsumexp(joint_logps, axis=0)
+
+    # We have to add dummy logps for the remaining value variables, otherwise AePPL will raise
+    return joint_logps, *(at.constant(0),) * (len(values) - 1)
diff --git a/pymc_experimental/tests/test_marginal_model.py b/pymc_experimental/tests/test_marginal_model.py
new file mode 100644
index 00000000..6048bc24
--- /dev/null
+++ b/pymc_experimental/tests/test_marginal_model.py
@@ -0,0 +1,410 @@
+import itertools
+from contextlib import suppress as does_not_warn
+
+import aesara.tensor as at
+import numpy as np
+import pandas as pd
+import pymc as pm
+import pytest
+from aeppl.logprob import _logprob
+from pymc import ImputationWarning, inputvars
+from pymc.distributions import transforms
+from pymc.util import UNSET
+from scipy.special import logsumexp
+
+from pymc_experimental.marginal_model import FiniteDiscreteMarginalRV, MarginalModel
+
+
+@pytest.fixture
+def disaster_model():
+    # fmt: off
+    disaster_data = pd.Series(
+        [4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,
+         3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,
+         2, 2, 3, 4, 2, 1, 3, np.nan, 2, 1, 1, 1, 1, 3, 0, 0,
+         1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,
+         0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,
+         3, 3, 1, np.nan, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,
+         0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1]
+    )
+    # fmt: on
+    years = np.arange(1851, 1962)
+
+    with MarginalModel() as disaster_model:
+        switchpoint = pm.DiscreteUniform("switchpoint", lower=years.min(), upper=years.max())
+        early_rate = pm.Exponential("early_rate", 1.0, initval=3)
+        late_rate = pm.Exponential("late_rate", 1.0, initval=1)
+        rate = pm.math.switch(switchpoint >= years, early_rate, late_rate)
+        with pytest.warns(ImputationWarning):
+            disasters = pm.Poisson("disasters", rate, observed=disaster_data)
+
+    return disaster_model, years
+
+
+@pytest.mark.filterwarnings("error")
+def test_marginalized_bernoulli_logp():
+    """Test logp of IR TestFiniteMarginalDiscreteRV directly"""
+    mu = at.vector("mu")
+
+    idx = pm.Bernoulli.dist(0.7, name="idx")
+    y = pm.Normal.dist(mu=mu[idx], sigma=1.0, name="y")
+    marginal_rv_node = FiniteDiscreteMarginalRV([mu], [idx, y], ndim_supp=None, n_updates=0,)(
+        mu
+    )[0].owner
+
+    y_vv = y.clone()
+    (logp,) = _logprob(
+        marginal_rv_node.op,
+        (y_vv,),
+        *marginal_rv_node.inputs,
+    )
+
+    ref_logp = pm.logp(pm.NormalMixture.dist(w=[0.3, 0.7], mu=mu, sigma=1.0), y_vv)
+    np.testing.assert_almost_equal(
+        logp.eval({mu: [-1, 1], y_vv: 2}),
+        ref_logp.eval({mu: [-1, 1], y_vv: 2}),
+    )
+
+
+@pytest.mark.filterwarnings("error")
+def test_marginalized_basic():
+    data = [2] * 5
+
+    with MarginalModel() as m:
+        sigma = pm.HalfNormal("sigma")
+        idx = pm.Categorical("idx", p=[0.1, 0.3, 0.6])
+        mu = at.switch(
+            at.eq(idx, 0),
+            -1.0,
+            at.switch(
+                at.eq(idx, 1),
+                0.0,
+                1.0,
+            ),
+        )
+        y = pm.Normal("y", mu=mu, sigma=sigma)
+        z = pm.Normal("z", y, observed=data)
+
+    m.marginalize([idx])
+    assert idx not in m.free_RVs
+    assert [rv.name for rv in m.marginalized_rvs] == ["idx"]
+
+    # Test logp
+    with pm.Model() as m_ref:
+        sigma = pm.HalfNormal("sigma")
+        y = pm.NormalMixture("y", w=[0.1, 0.3, 0.6], mu=[-1, 0, 1], sigma=sigma)
+        z = pm.Normal("z", y, observed=data)
+
+    test_point = m_ref.initial_point()
+    ref_logp = m_ref.compile_logp()(test_point)
+    ref_dlogp = m_ref.compile_dlogp([m_ref["y"]])(test_point)
+
+    # Assert we can marginalize and unmarginalize internally non-destructively
+    for i in range(3):
+        np.testing.assert_almost_equal(
+            m.compile_logp()(test_point),
+            ref_logp,
+        )
+        np.testing.assert_almost_equal(
+            m.compile_dlogp([m["y"]])(test_point),
+            ref_dlogp,
+        )
+
+
+@pytest.mark.filterwarnings("error")
+def test_multiple_independent_marginalized_rvs():
+    with MarginalModel() as m:
+        sigma = pm.HalfNormal("sigma")
+        idx1 = pm.Bernoulli("idx1", p=0.75)
+        x = pm.Normal("x", mu=idx1, sigma=sigma)
+        idx2 = pm.Bernoulli("idx2", p=0.75, shape=(5,))
+        y = pm.Normal("y", mu=(idx2 * 2 - 1), sigma=sigma, shape=(5,))
+
+    m.marginalize([idx1, idx2])
+    m["x"].owner is not m["y"].owner
+    _m = m.clone()._marginalize()
+    _m["x"].owner is not _m["y"].owner
+
+    with pm.Model() as m_ref:
+        sigma = pm.HalfNormal("sigma")
+        x = pm.NormalMixture("x", w=[0.25, 0.75], mu=[0, 1], sigma=sigma)
+        y = pm.NormalMixture("y", w=[0.25, 0.75], mu=[-1, 1], sigma=sigma, shape=(5,))
+
+    # Test logp
+    test_point = m_ref.initial_point()
+    x_logp, y_logp = m.compile_logp(vars=[m["x"], m["y"]], sum=False)(test_point)
+    x_ref_log, y_ref_logp = m_ref.compile_logp(vars=[m_ref["x"], m_ref["y"]], sum=False)(test_point)
+    np.testing.assert_array_almost_equal(x_logp, x_ref_log.sum())
+    np.testing.assert_array_almost_equal(y_logp, y_ref_logp)
+
+
+@pytest.mark.filterwarnings("error")
+def test_multiple_dependent_marginalized_rvs():
+    """Test that marginalization works when there is more than one dependent RV"""
+    with MarginalModel() as m:
+        sigma = pm.HalfNormal("sigma")
+        idx = pm.Bernoulli("idx", p=0.75)
+        x = pm.Normal("x", mu=idx, sigma=sigma)
+        y = pm.Normal("y", mu=(idx * 2 - 1), sigma=sigma, shape=(5,))
+
+    ref_logp_x_y_fn = m.compile_logp([idx, x, y])
+
+    with pytest.warns(UserWarning, match="There are multiple dependent variables"):
+        m.marginalize([idx])
+
+    m["x"].owner is not m["y"].owner
+    _m = m.clone()._marginalize()
+    _m["x"].owner is _m["y"].owner
+
+    tp = m.initial_point()
+    ref_logp_x_y = logsumexp([ref_logp_x_y_fn({**tp, **{"idx": idx}}) for idx in (0, 1)])
+    logp_x_y = m.compile_logp([x, y])(tp)
+    np.testing.assert_array_almost_equal(logp_x_y, ref_logp_x_y)
+
+
+@pytest.mark.filterwarnings("error")
+def test_nested_marginalized_rvs():
+    """Test that marginalization works when there are nested marginalized RVs"""
+
+    with MarginalModel() as m:
+        sigma = pm.HalfNormal("sigma")
+
+        idx = pm.Bernoulli("idx", p=0.75)
+        dep = pm.Normal("dep", mu=at.switch(at.eq(idx, 0), -1000, 1000), sigma=sigma)
+
+        sub_idx = pm.Bernoulli("sub_idx", p=at.switch(at.eq(idx, 0), 0.15, 0.95), shape=(5,))
+        sub_dep = pm.Normal("sub_dep", mu=dep + sub_idx * 100, sigma=sigma, shape=(5,))
+
+    ref_logp_fn = m.compile_logp(vars=[idx, dep, sub_idx, sub_dep])
+
+    with pytest.warns(UserWarning, match="There are multiple dependent variables"):
+        m.marginalize([idx, sub_idx])
+
+    assert set(m.marginalized_rvs) == {idx, sub_idx}
+
+    # Test logp
+    test_point = m.initial_point()
+    test_point["dep"] = 1000
+    test_point["sub_dep"] = np.full((5,), 1000 + 100)
+
+    ref_logp = [
+        ref_logp_fn({**test_point, **{"idx": idx, "sub_idx": np.array(sub_idxs)}})
+        for idx in (0, 1)
+        for sub_idxs in itertools.product((0, 1), repeat=5)
+    ]
+    logp = m.compile_logp(vars=[dep, sub_dep])(test_point)
+
+    np.testing.assert_almost_equal(
+        logp,
+        logsumexp(ref_logp),
+    )
+
+
+@pytest.mark.filterwarnings("error")
+def test_marginalized_change_point_model(disaster_model):
+    m, years = disaster_model
+
+    ip = m.initial_point()
+    ip.pop("switchpoint")
+    ref_logp_fn = m.compile_logp(
+        [m["switchpoint"], m["disasters_observed"], m["disasters_missing"]]
+    )
+    ref_logp = logsumexp([ref_logp_fn({**ip, **{"switchpoint": year}}) for year in years])
+
+    with pytest.warns(UserWarning, match="There are multiple dependent variables"):
+        m.marginalize(m["switchpoint"])
+
+    logp = m.compile_logp([m["disasters_observed"], m["disasters_missing"]])(ip)
+    np.testing.assert_almost_equal(logp, ref_logp)
+
+
+@pytest.mark.slow
+@pytest.mark.filterwarnings("error")
+def test_marginalized_change_point_model_sampling(disaster_model):
+    m, _ = disaster_model
+
+    rng = np.random.default_rng(211)
+
+    with m:
+        before_marg = pm.sample(chains=2, random_seed=rng).posterior.stack(sample=("draw", "chain"))
+
+    with pytest.warns(UserWarning, match="There are multiple dependent variables"):
+        m.marginalize([m["switchpoint"]])
+
+    with m:
+        after_marg = pm.sample(chains=2, random_seed=rng).posterior.stack(sample=("draw", "chain"))
+
+    np.testing.assert_allclose(
+        before_marg["early_rate"].mean(), after_marg["early_rate"].mean(), rtol=1e-2
+    )
+    np.testing.assert_allclose(
+        before_marg["late_rate"].mean(), after_marg["late_rate"].mean(), rtol=1e-2
+    )
+    np.testing.assert_allclose(
+        before_marg["disasters_missing"].mean(), after_marg["disasters_missing"].mean(), rtol=1e-2
+    )
+
+
+@pytest.mark.filterwarnings("error")
+def test_not_supported_marginalized():
+    """Marginalized graphs with non-Elemwise Operations are not supported as they
+    would violate the batching logp assumption"""
+    mu = at.constant([-1, 1])
+
+    # Allowed, as only elemwise operations connect idx to y
+    with MarginalModel() as m:
+        p = pm.Beta("p", 1, 1)
+        idx = pm.Bernoulli("idx", p=p, size=2)
+        y = pm.Normal("y", mu=pm.math.switch(idx, 0, 1))
+        m.marginalize([idx])
+
+    # ALlowed, as index operation does not connext idx to y
+    with MarginalModel() as m:
+        p = pm.Beta("p", 1, 1)
+        idx = pm.Bernoulli("idx", p=p, size=2)
+        y = pm.Normal("y", mu=pm.math.switch(idx, mu[0], mu[1]))
+        m.marginalize([idx])
+
+    # Not allowed, as index operation  connects idx to y
+    with MarginalModel() as m:
+        p = pm.Beta("p", 1, 1)
+        idx = pm.Bernoulli("idx", p=p, size=2)
+        # Not allowed
+        y = pm.Normal("y", mu=mu[idx])
+        with pytest.raises(NotImplementedError):
+            m.marginalize(idx)
+
+    # Not allowed, as index operation  connects idx to y, even though there is a
+    # pure Elemwise connection between the two
+    with MarginalModel() as m:
+        p = pm.Beta("p", 1, 1)
+        idx = pm.Bernoulli("idx", p=p, size=2)
+        y = pm.Normal("y", mu=mu[idx] + idx)
+        with pytest.raises(NotImplementedError):
+            m.marginalize(idx)
+
+    # Multivariate dependent RVs not supported
+    with MarginalModel() as m:
+        x = pm.Bernoulli("x", p=0.7)
+        y = pm.Dirichlet("y", a=pm.math.switch(x, [1, 1, 1], [10, 10, 10]))
+        with pytest.raises(
+            NotImplementedError,
+            match="Marginalization of withe dependent Multivariate RVs not implemented",
+        ):
+            m.marginalize(x)
+
+
+@pytest.mark.filterwarnings("error")
+def test_marginalized_deterministic_and_potential():
+    with MarginalModel() as m:
+        x = pm.Bernoulli("x", p=0.7)
+        y = pm.Normal("y", x)
+        z = pm.Normal("z", x)
+        det = pm.Deterministic("det", y + z)
+        pot = pm.Potential("pot", y + z + 1)
+
+    with pytest.warns(UserWarning, match="There are multiple dependent variables"):
+        m.marginalize([x])
+
+    y_draw, z_draw, det_draw, pot_draw = pm.draw([y, z, det, pot], draws=5)
+    np.testing.assert_almost_equal(y_draw + z_draw, det_draw)
+    np.testing.assert_almost_equal(det_draw, pot_draw - 1)
+
+    y_value = m.rvs_to_values[y]
+    z_value = m.rvs_to_values[z]
+    det_value, pot_value = m.replace_rvs_by_values([det, pot])
+    assert set(inputvars([det_value, pot_value])) == {y_value, z_value}
+    assert det_value.eval({y_value: 2, z_value: 5}) == 7
+    assert pot_value.eval({y_value: 2, z_value: 5}) == 8
+
+
+@pytest.mark.filterwarnings("error")
+def test_not_supported_marginalized_deterministic_and_potential():
+    with MarginalModel() as m:
+        x = pm.Bernoulli("x", p=0.7)
+        y = pm.Normal("y", x)
+        det = pm.Deterministic("det", x + y)
+
+    with pytest.raises(
+        NotImplementedError, match="Cannot marginalize x due to dependent Deterministic det"
+    ):
+        m.marginalize([x])
+
+    with MarginalModel() as m:
+        x = pm.Bernoulli("x", p=0.7)
+        y = pm.Normal("y", x)
+        pot = pm.Potential("pot", x + y)
+
+    with pytest.raises(
+        NotImplementedError, match="Cannot marginalize x due to dependent Potential pot"
+    ):
+        m.marginalize([x])
+
+
+@pytest.mark.filterwarnings("error")
+@pytest.mark.parametrize(
+    "transform, expected_warning",
+    (
+        (None, does_not_warn()),
+        pytest.param(
+            UNSET,
+            pytest.warns(
+                UserWarning, match="Transforms for variables that depend on marginalized RVs"
+            ),
+            marks=pytest.mark.xfail(
+                reason="AePPL transform rewrite does not support multiple_output nodes",
+                raises=AssertionError,
+            ),
+        ),
+        pytest.param(
+            transforms.log,
+            pytest.warns(
+                UserWarning, match="Transforms for variables that depend on marginalized RVs"
+            ),
+            marks=pytest.mark.xfail(
+                reason="AePPL transform rewrite does not support multiple_output nodes",
+                raises=AssertionError,
+            ),
+        ),
+    ),
+)
+def test_marginalized_transforms(transform, expected_warning):
+    w = [0.1, 0.3, 0.6]
+    data = [0, 5, 10]
+    initval = 0.5  # Value that will be negative on the unconstrained space
+
+    with pm.Model() as m_ref:
+        sigma = pm.Mixture(
+            "sigma",
+            w=w,
+            comp_dists=pm.HalfNormal.dist([1, 2, 3]),
+            initval=initval,
+            transform=transform,
+        )
+        y = pm.Normal("y", 0, sigma, observed=data)
+
+    with MarginalModel() as m:
+        idx = pm.Categorical("idx", p=w)
+        sigma = pm.HalfNormal(
+            "sigma",
+            at.switch(
+                at.eq(idx, 0),
+                1,
+                at.switch(
+                    at.eq(idx, 1),
+                    2,
+                    3,
+                ),
+            ),
+            initval=initval,
+            transform=transform,
+        )
+        y = pm.Normal("y", 0, sigma, observed=data)
+
+    with expected_warning:
+        m.marginalize([idx])
+
+    ip = m.initial_point()
+    if transform is not None:
+        assert "sigma_log__" in ip
+    np.testing.assert_allclose(m.compile_logp()(ip), m_ref.compile_logp()(ip))
diff --git a/requirements.txt b/requirements.txt
index 48d2355a..8d19789a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1 +1 @@
-pymc>=4.3.0
+pymc>=4.4.0