diff --git a/Python/bayesian_cuped.ipynb b/Python/bayesian_cuped.ipynb
new file mode 100644
index 0000000..8b089a1
--- /dev/null
+++ b/Python/bayesian_cuped.ipynb
@@ -0,0 +1,518 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import arviz as az\n",
+    "import jax.numpy as jnp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import numpyro\n",
+    "import numpyro.distributions as dist\n",
+    "import pandas as pd\n",
+    "from jax import random\n",
+    "from numpyro.infer import MCMC, NUTS\n",
+    "\n",
+    "numpyro.set_host_device_count(n=4)\n",
+    "\n",
+    "az.style.use(\"arviz-darkgrid\")\n",
+    "plt.rcParams[\"figure.figsize\"] = [12, 7]\n",
+    "plt.rcParams[\"figure.dpi\"] = 100\n",
+    "plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
+    "\n",
+    "rng_key = random.PRNGKey(seed=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class dgp_cuped:\n",
+    "    \"\"\"\n",
+    "    Data Generating Process: CUPED\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, alpha=5, beta=0, gamma=3, delta=2):\n",
+    "        self.alpha = alpha\n",
+    "        self.beta = beta\n",
+    "        self.gamma = gamma\n",
+    "        self.delta = delta\n",
+    "\n",
+    "    def generate_data(self, N=100, seed=1):\n",
+    "        np.random.seed(seed)\n",
+    "\n",
+    "        # Individuals\n",
+    "        i = range(1, N + 1)\n",
+    "\n",
+    "        # Treatment status\n",
+    "        d = np.random.binomial(1, 0.5, N)\n",
+    "\n",
+    "        # Individual outcome pre-treatment\n",
+    "        y0 = self.alpha + self.beta * d + np.random.normal(0, 1, N)\n",
+    "        y1 = y0 + self.gamma + self.delta * d + np.random.normal(0, 1, N)\n",
+    "\n",
+    "        # Generate the dataframe\n",
+    "        df = pd.DataFrame({\"i\": i, \"ad_campaign\": d, \"revenue0\": y0, \"revenue1\": y1})\n",
+    "\n",
+    "        return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>i</th>\n",
+       "      <th>ad_campaign</th>\n",
+       "      <th>revenue0</th>\n",
+       "      <th>revenue1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.315635</td>\n",
+       "      <td>8.359304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.977799</td>\n",
+       "      <td>7.751485</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.693796</td>\n",
+       "      <td>9.025253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.827975</td>\n",
+       "      <td>8.540667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.230095</td>\n",
+       "      <td>8.910165</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   i  ad_campaign  revenue0  revenue1\n",
+       "0  1            0  5.315635  8.359304\n",
+       "1  2            1  2.977799  7.751485\n",
+       "2  3            0  4.693796  9.025253\n",
+       "3  4            0  5.827975  8.540667\n",
+       "4  5            0  5.230095  8.910165"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = dgp_cuped().generate_data()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.7914301325347406"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    np.mean(df.loc[df.ad_campaign == True, \"revenue1\"])\n",
+    "    - np.mean(df.loc[df.ad_campaign == False, \"revenue1\"])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ad_campaign = df.ad_campaign.to_numpy()\n",
+    "revenue0 = df.revenue0.to_numpy()\n",
+    "revenue1 = df.revenue1.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cuped_model(ad_campaign, revenue0, revenue1):\n",
+    "    n_samples = len(ad_campaign)\n",
+    "    intercept_target = numpyro.sample(\"intercept_target\", dist.Normal(0, 2))\n",
+    "    theta = numpyro.sample(\"theta\", dist.Normal(0, 2))\n",
+    "    sigma_theta = numpyro.sample(\"sigma_theta\", dist.HalfCauchy(2))\n",
+    "\n",
+    "    mu_target = intercept_target + theta * revenue0\n",
+    "\n",
+    "    with numpyro.plate(\"target_conditioning\", n_samples):\n",
+    "        numpyro.sample(\n",
+    "            \"revenue1_pred\", dist.Normal(mu_target, sigma_theta), obs=revenue1\n",
+    "        )\n",
+    "\n",
+    "    revenue_cuped = numpyro.deterministic(\n",
+    "        \"revenue_cuped\", revenue1 - theta * (revenue0 - jnp.mean(revenue0))\n",
+    "    )\n",
+    "\n",
+    "    intercept_cuped = numpyro.sample(\"intercept_cuped\", dist.Normal(0, 2))\n",
+    "    beta_cuped = numpyro.sample(\"beta_cuped\", dist.Normal(0, 3))\n",
+    "\n",
+    "    with numpyro.plate(\"cuped_conditioning\", len(ad_campaign)):\n",
+    "        numpyro.sample(\n",
+    "            \"revenue_cuped_pred\",\n",
+    "            dist.Normal(intercept_cuped + beta_cuped * ad_campaign, 1),\n",
+    "            obs=revenue_cuped,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
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+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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+       " -->\n",
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+       " viewBox=\"0.00 0.00 910.25 260.50\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
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+       "<g id=\"clust1\" class=\"cluster\">\n",
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+       "<g id=\"clust2\" class=\"cluster\">\n",
+       "<title>cluster_cuped_conditioning</title>\n",
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+       "</g>\n",
+       "<!-- intercept_target -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>intercept_target</title>\n",
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+       "</g>\n",
+       "<!-- revenue1_pred -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>revenue1_pred</title>\n",
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+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>intercept_target&#45;&gt;revenue1_pred</title>\n",
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+       "<title>revenue_cuped</title>\n",
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+       "</g>\n",
+       "<!-- theta&#45;&gt;revenue_cuped -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>theta&#45;&gt;revenue_cuped</title>\n",
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+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>sigma_theta</title>\n",
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+       "</g>\n",
+       "<!-- sigma_theta&#45;&gt;revenue1_pred -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>sigma_theta&#45;&gt;revenue1_pred</title>\n",
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+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>intercept_cuped</title>\n",
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+       "</g>\n",
+       "<!-- revenue_cuped_pred -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>revenue_cuped_pred</title>\n",
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+       "</g>\n",
+       "<!-- intercept_cuped&#45;&gt;revenue_cuped_pred -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>intercept_cuped&#45;&gt;revenue_cuped_pred</title>\n",
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+       "<title>beta_cuped</title>\n",
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+       "</g>\n",
+       "<!-- beta_cuped&#45;&gt;revenue_cuped_pred -->\n",
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+       "<title>beta_cuped&#45;&gt;revenue_cuped_pred</title>\n",
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+       "<title>distribution_description_node</title>\n",
+       "<text text-anchor=\"start\" x=\"723.25\" y=\"-235.2\" font-family=\"Times,serif\" font-size=\"14.00\">intercept_target ~ Normal</text>\n",
+       "<text text-anchor=\"start\" x=\"723.25\" y=\"-218.7\" font-family=\"Times,serif\" font-size=\"14.00\">theta ~ Normal</text>\n",
+       "<text text-anchor=\"start\" x=\"723.25\" y=\"-202.2\" font-family=\"Times,serif\" font-size=\"14.00\">sigma_theta ~ HalfCauchy</text>\n",
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x30feee090>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numpyro.render_model(\n",
+    "    cuped_model,\n",
+    "    model_args=(ad_campaign, revenue0, revenue1),\n",
+    "    render_distributions=True,\n",
+    "    render_params=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6654e04b092b4071afaa739dd484eb10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "528b5998c76842a4ba318b584bfba57f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "732b4df09f32421eab8d68c5552f9744",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "420f01d6ea964309ae6ff7445ff9fd27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sampler = NUTS(cuped_model)\n",
+    "mcmc = MCMC(\n",
+    "    sampler=sampler,\n",
+    "    num_warmup=1_500,\n",
+    "    num_samples=2_000,\n",
+    "    num_chains=4,\n",
+    ")\n",
+    "rng_key, rng_subkey = random.split(rng_key)\n",
+    "mcmc.run(rng_subkey, ad_campaign, revenue0, revenue1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idata = az.from_numpyro(mcmc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "az.plot_posterior(idata, var_names=[\"theta\"], ax=ax)\n",
+    "ax.set_title(r\"Posterior distribution of $\\theta$\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "az.plot_posterior(idata, var_names=[\"beta_cuped\"], ref_val=2.0, ax=ax)\n",
+    "ax.set_title(\"Posterior distribution of Revenue CUPED Effect Estimate\");"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "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": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}