diff --git a/notebooks/book1/04/bootstrap_demo_bernoulli.ipynb b/notebooks/book1/04/bootstrap_demo_bernoulli.ipynb
index b4305b9eae..b8d3fd2650 100644
--- a/notebooks/book1/04/bootstrap_demo_bernoulli.ipynb
+++ b/notebooks/book1/04/bootstrap_demo_bernoulli.ipynb
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "fdba4e93",
    "metadata": {
     "scrolled": false
@@ -52,50 +52,82 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/patel_zeel/probml-utils/probml_utils/plotting.py:25: UserWarning: LATEXIFY environment variable not set, not latexifying\n",
+      "/opt/anaconda3/lib/python3.8/site-packages/probml_utils/plotting.py:25: UserWarning: LATEXIFY environment variable not set, not latexifying\n",
       "  warnings.warn(\"LATEXIFY environment variable not set, not latexifying\")\n",
-      "/home/patel_zeel/probml-utils/probml_utils/plotting.py:84: UserWarning: set FIG_DIR environment variable to save figures\n",
+      "/opt/anaconda3/lib/python3.8/site-packages/probml_utils/plotting.py:84: UserWarning: set FIG_DIR environment variable to save figures\n",
       "  warnings.warn(\"set FIG_DIR environment variable to save figures\")\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -144,6 +176,15 @@
     "    bootstrap_samples = jax.vmap(generative_fn)(bootstrap_keys)\n",
     "    bootstrap_mle = jax.vmap(mle_fn)(bootstrap_samples)\n",
     "\n",
+    "    plt.figure()\n",
+    "    plt.title(f\"Bootstrap (Non-parametric): Number of data points = {n_data_points}\")\n",
+    "    plt.hist(bootstrap_mle, bins=10)\n",
+    "    plt.vlines(mle, *plt.ylim(), zorder=10, color=\"r\", linestyle=\"--\", label=r\"MLE ($\\hat{\\theta}$)\")\n",
+    "    plt.xlabel(r\"$\\hat{\\theta}^s$\")\n",
+    "    plt.xlim(0, 1)\n",
+    "    plt.legend(loc=\"upper left\")\n",
+    "    savefig(f\"bootstrap_demo_nonparam_bootstrap_{n_data_points}\")\n",
+    "\n",
     "    # Bayesian estimate\n",
     "    counts_of_1 = jnp.sum(data)\n",
     "    counts_of_0 = len(data) - counts_of_1\n",
@@ -168,9 +209,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:pyprobml]",
+   "display_name": "base",
    "language": "python",
-   "name": "conda-env-pyprobml-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -182,7 +223,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.13"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/scratchpad.ipynb b/scratchpad.ipynb
index bf2b9c1124..3515088b0f 100644
--- a/scratchpad.ipynb
+++ b/scratchpad.ipynb
@@ -1,193 +1,443 @@
 {
-  "cells": [
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "done\n"
-          ]
-        }
-      ],
-      "source": [
-        "import os \n",
-        "# 2x 2 chips (4 cores) per process:\n",
-        "os.environ[\"TPU_CHIPS_PER_HOST_BOUNDS\"] = \"1,2,1\"\n",
-        "os.environ[\"TPU_HOST_BOUNDS\"] = \"1,1,1\"\n",
-        "# Different per process:\n",
-        "os.environ[\"TPU_VISIBLE_DEVICES\"] = \"0,1\" # Change to \"2,3\" for the second machine\n",
-        "# Pick a unique port per process\n",
-        "os.environ[\"TPU_MESH_CONTROLLER_ADDRESS\"] = \"localhost:8476\"\n",
-        "os.environ[\"TPU_MESH_CONTROLLER_PORT\"] = \"8476\"\n",
-        "\n",
-        "print('done')"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "# 2x 2 chips (4 cores) per process:\n",
+    "os.environ[\"TPU_CHIPS_PER_HOST_BOUNDS\"] = \"1,2,1\"\n",
+    "os.environ[\"TPU_HOST_BOUNDS\"] = \"1,1,1\"\n",
+    "# Different per process:\n",
+    "os.environ[\"TPU_VISIBLE_DEVICES\"] = \"0,1\"  # Change to \"2,3\" for the second machine\n",
+    "# Pick a unique port per process\n",
+    "os.environ[\"TPU_MESH_CONTROLLER_ADDRESS\"] = \"localhost:8476\"\n",
+    "os.environ[\"TPU_MESH_CONTROLLER_PORT\"] = \"8476\"\n",
+    "\n",
+    "print(\"done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[CpuDevice(id=0)]\n"
-          ]
-        }
-      ],
-      "source": [
-        "import jax\n",
-        "print(jax.devices())#"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.5\n",
+      "1.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1050 / 700.0)\n",
+    "print(1350 / 900)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Silence WARNING:root:The use of `check_types` is deprecated and does not have any effect.\n",
-        "# https://github.com/tensorflow/probability/issues/1523\n",
-        "import logging\n",
-        "logger = logging.getLogger() \n",
-        "\n",
-        "class CheckTypesFilter(logging.Filter):\n",
-        "    def filter(self, record):\n",
-        "        return \"check_types\" not in record.getMessage()\n",
-        "\n",
-        "\n",
-        "logger.addFilter(CheckTypesFilter())"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CpuDevice(id=0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import jax\n",
+    "\n",
+    "print(jax.devices())  #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Silence WARNING:root:The use of `check_types` is deprecated and does not have any effect.\n",
+    "# https://github.com/tensorflow/probability/issues/1523\n",
+    "import logging\n",
+    "\n",
+    "logger = logging.getLogger()\n",
+    "\n",
+    "\n",
+    "class CheckTypesFilter(logging.Filter):\n",
+    "    def filter(self, record):\n",
+    "        return \"check_types\" not in record.getMessage()\n",
+    "\n",
+    "\n",
+    "logger.addFilter(CheckTypesFilter())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Unnamed: 0', 'COUNTRY', 'CHOCOLATE', 'NOBEL', 'POPULATION',\n",
+      "       'INTERNET'],\n",
+      "      dtype='object')\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[[0]\n",
-            " [0]\n",
-            " [0]\n",
-            " [0]\n",
-            " [0]\n",
-            " [0]\n",
-            " [0]\n",
-            " [1]\n",
-            " [1]\n",
-            " [1]\n",
-            " [1]\n",
-            " [1]\n",
-            " [1]\n",
-            " [1]]\n"
-          ]
-        }
+     "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>Unnamed: 0</th>\n",
+       "      <th>COUNTRY</th>\n",
+       "      <th>CHOCOLATE</th>\n",
+       "      <th>NOBEL</th>\n",
+       "      <th>POPULATION</th>\n",
+       "      <th>INTERNET</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Australia</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>22</td>\n",
+       "      <td>79.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>24.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>79.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Belgium</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>8.6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Brazil</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>197</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Canada</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>83.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "# Check that RNG works\n",
-        "# Context: https://github.com/google/jax/issues/7896\n",
-        "import jax\n",
-        "import jax.numpy as jnp\n",
-        "\n",
-        "# sample from a Markov chain\n",
-        "init_dist = jnp.array([0.8, 0.2])\n",
-        "trans_mat = jnp.array([[0.9, 0.1], [0.5, 0.5]])\n",
-        "rng_key = jax.random.PRNGKey(0)\n",
-        "from jax.scipy.special import logit\n",
-        "seq_len = 15\n",
-        "\n",
-        "initial_state = jax.random.categorical(rng_key, logits=logit(init_dist), shape=(1,))\n",
-        "\n",
-        "def draw_state(prev_state, key):\n",
-        "        logits = logit(trans_mat[:, prev_state])\n",
-        "        state = jax.random.categorical(key, logits=logits.flatten(), shape=(1,))\n",
-        "        return state, state\n",
-        "\n",
-        "        rng_key, rng_state, rng_obs = jax.random.split(rng_key, 3)\n",
-        "        keys = jax.random.split(rng_state, seq_len - 1)\n",
-        "\n",
-        "        final_state, states = jax.lax.scan(draw_state, initial_state, keys)\n",
-        "\n",
-        "        print(states)\n",
-        "            \n",
-        "rng_key, rng_state, rng_obs = jax.random.split(rng_key, 3)\n",
-        "keys = jax.random.split(rng_state, seq_len - 1)\n",
-        "\n",
-        "final_state, states = jax.lax.scan(draw_state, initial_state, keys)\n",
-        "\n",
-        "print(states)"
+      "text/plain": [
+       "   Unnamed: 0    COUNTRY  CHOCOLATE  NOBEL  POPULATION  INTERNET\n",
+       "0           1  Australia        4.5    5.5          22      79.5\n",
+       "1           2    Austria       10.2   24.3           8      79.8\n",
+       "2           3    Belgium        4.4    8.6          11      78.0\n",
+       "3           4     Brazil        2.9    0.1         197      45.0\n",
+       "4           5     Canada        3.9    6.1          34      83.0"
       ]
-    },
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df0 = pd.read_csv(\"/Users/kpmurphy/Misc/chocolate-nobel.csv\")\n",
+    "print(df0.columns)\n",
+    "df0.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "The autoreload extension is already loaded. To reload it, use:\n",
-            "  %reload_ext autoreload\n"
-          ]
-        }
-      ],
-      "source": [
-        "%load_ext autoreload\n",
-        "%autoreload 2"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        country     x     y\n",
+      "0     Australia   4.5   5.5\n",
+      "1       Austria  10.2  24.3\n",
+      "2       Belgium   4.4   8.6\n",
+      "3        Brazil   2.9   0.1\n",
+      "4        Canada   3.9   6.1\n",
+      "5         China   0.7   0.1\n",
+      "6       Denmark   8.5  25.3\n",
+      "7       Finland   7.3   7.6\n",
+      "8        France   6.3   9.0\n",
+      "9       Germany  11.6  12.7\n",
+      "10       Greece   2.5   1.9\n",
+      "11      Ireland   8.8  12.7\n",
+      "12        Italy   3.7   3.3\n",
+      "13        Japan   1.8   1.5\n",
+      "14  Netherlands   4.5  11.4\n",
+      "15       Norway   9.4  25.5\n",
+      "16       Poland   3.6   3.1\n",
+      "17     Portugal   2.0   1.9\n",
+      "18        Spain   3.6   1.7\n",
+      "20  Switzerland  11.9  31.5\n",
+      "21           UK   9.7  18.9\n",
+      "22          USA   5.3  10.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "def cleanup_columns(df):\n",
+    "    df = df.drop(columns=[\"Unnamed: 0\", \"POPULATION\", \"INTERNET\"])\n",
+    "    df.columns = map(str.lower, df.columns)\n",
+    "    df = df.rename(columns={\"chocolate\": \"x\", \"nobel\": \"y\"})\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def drop_sweden(df):\n",
+    "    return df[df.country != \"Sweden\"]\n",
+    "\n",
+    "\n",
+    "df = drop_sweden(cleanup_columns(df0))\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def add_noise(df):\n",
+    "    N = len(df)\n",
+    "    np.random.seed(0)\n",
+    "    noise_x = np.random.normal(0, 1, size=N)\n",
+    "    noise_y = np.random.normal(0, 1, size=N)\n",
+    "    df[\"x\"] = df[\"x\"] + noise_x\n",
+    "    df[\"y\"] = (df[\"y\"] * 100) + noise_y\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        country          x            y\n",
+      "0     Australia   6.264052   550.864436\n",
+      "1       Austria  10.600157  2429.257835\n",
+      "2       Belgium   5.378738   862.269755\n",
+      "3        Brazil   5.140893     8.545634\n",
+      "4        Canada   5.767558   610.045759\n",
+      "5         China  -0.277278     9.812816\n",
+      "6       Denmark   9.450088  2531.532779\n",
+      "7       Finland   7.148643   761.469359\n",
+      "8        France   6.196781   900.154947\n",
+      "9       Germany  12.010599  1270.378163\n",
+      "10       Greece   2.644044   189.112214\n",
+      "11      Ireland  10.254274  1268.019204\n",
+      "12        Italy   4.461038   329.652088\n",
+      "13        Japan   1.921675   150.156349\n",
+      "14  Netherlands   4.943863  1141.230291\n",
+      "15       Norway   9.733674  2551.202380\n",
+      "16       Poland   5.094079   309.612673\n",
+      "17     Portugal   1.794842   189.697697\n",
+      "18        Spain   3.913068   168.951447\n",
+      "20  Switzerland  11.045904  3148.579982\n",
+      "21           UK   7.147010  1888.293730\n",
+      "22          USA   5.953619  1081.950775\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"/Users/kpmurphy/Misc/chocolate-nobel.csv\").pipe(cleanup_columns).pipe(drop_sweden).pipe(add_noise)\n",
+    "\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: xlabel='x', ylabel='y'>"
       ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "colab": {
-      "include_colab_link": true,
-      "name": "scratchpad",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3.8.5 ('base')",
-      "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": "3.8.5"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
-      }
+   ],
+   "source": [
+    "df.plot.scatter(x=\"x\", y=\"y\", label=\"country\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
+   ],
+   "source": [
+    "# Label points with country names\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.scatter(df[\"x\"], df[\"y\"], s=100, alpha=0.5)\n",
+    "for i, txt in enumerate(df[\"country\"]):\n",
+    "    plt.annotate(txt, (df[\"x\"].iat[i], df[\"y\"].iat[i]), fontsize=10, ha=\"right\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"/Users/kpmurphy/Misc/noisy-choc.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "scratchpad",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.5 ('base')",
+   "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": "3.9.6"
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
+  "vscode": {
+   "interpreter": {
+    "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/tikz/POMDP_tikz2.pdf b/tikz/POMDP_tikz2.pdf
new file mode 100644
index 0000000000..a0f761e1a4
Binary files /dev/null and b/tikz/POMDP_tikz2.pdf differ
diff --git a/tikz/POMDP_tikz2.tex b/tikz/POMDP_tikz2.tex
new file mode 100644
index 0000000000..9d9a183be1
--- /dev/null
+++ b/tikz/POMDP_tikz2.tex
@@ -0,0 +1,55 @@
+\documentclass[tikz, border=1cm]{standalone}
+% main document, called main.tex
+\usepackage{tikz}
+\usetikzlibrary{bayesnet}
+
+\begin{document}
+% \title{sequential_prediction}
+% \author{Dhruv Patel}
+% \date{July 2022}
+\tikzstyle{emptynode} = [rectangle, draw=black!0, minimum size=3mm]
+\tikzstyle{rect} = [rectangle, draw=black!90, minimum size=3mm]
+\tikzstyle{diam} = [diamond, draw=black!90, minimum width=1.5mm, minimum height = 1.5mm]
+\begin{tikzpicture}
+	
+	\node[latent]  (s0) {$s_0$};
+	\node[latent, right= 2.4cm of s0](s1){$s_1$};
+	\node[latent, right= 2.4cm of s1](s2){$s_2$};
+
+	\node[obs, below= 2cm of s0](x0){$x_0$};
+	\node[obs, below= 2cm of s1](x1){$x_1$};
+	\node[obs, below= 2cm of s2](x2){$x_2$};
+
+	\node[latent, below= 1cm of x0](b0){$b_0$};
+	\node[latent, below= 1cm of x1](b1){$b_1$};
+	\node[latent, below= 1cm of x2](b2){$b_2$};
+        
+	\node[rect, right = 0.8cm of x0](a0){$a_0$};
+	\node[rect, right = 0.8cm of x1](a1){$a_1$};
+        
+	\node[diam, above = 0.6 of a0](r0){$r_0$};
+	\node[diam, above = 0.6 of a1](r1){$r_1$};
+
+        \node[emptynode, right= 2cm of s2](e1){$\cdots$};
+	\node[emptynode, right= 2cm of b2](e2){$\cdots$};
+	
+	\edge{s0}{x0,s1,r0};
+	\edge{x0}{b0};
+	\edge{b0}{b1,a0};
+	\edge{b1}{b2};
+	\edge{a0}{r0,s1};
+	\edge{s1}{r1, x1, s2};
+	\edge{b1}{b2, a1};
+	\edge{s2}{x2};
+	\edge{x2,r1,a1}{b2};
+	\edge{a1}{r1,s2};
+	\edge{a0,r0,x1}{b1};
+	\edge{s2}{e1};
+	\edge{b2}{e2};
+    \node[draw=black,thin,fit=(s0)(s1)(s2), rectangle,inner sep=10pt](rect1)  {};
+    \node[const, above = 2.9cm of a1](){environment};
+    \node[draw=black,thin,fit=(b0)(b1)(b2), rectangle,inner sep=10pt](rect1)  {};
+    \node[const, below = 1.9cm of a1](){agent};
+
+\end{tikzpicture}
+\end{document}
diff --git a/tikz/bandits2.pdf b/tikz/bandits2.pdf
new file mode 100644
index 0000000000..ba9567ca00
Binary files /dev/null and b/tikz/bandits2.pdf differ
diff --git a/tikz/bandits2.tex b/tikz/bandits2.tex
new file mode 100644
index 0000000000..c7c5cdecc7
--- /dev/null
+++ b/tikz/bandits2.tex
@@ -0,0 +1,55 @@
+ \documentclass[tikz, border=1cm]{standalone}
+% main document, called main.tex
+\usepackage{tikz}
+\usetikzlibrary{bayesnet}
+
+\begin{document}
+% \title{sequential_prediction}
+% \author{Dhruv Patel}
+% \date{July 2022}
+\tikzstyle{emptynode} = [rectangle, draw=black!0, minimum size=3mm]
+\tikzstyle{rect} = [rectangle, draw=black!90, minimum size=3mm]
+\tikzstyle{diam} = [diamond, draw=black!90, minimum width=1.5mm, minimum height = 1.5mm]
+\begin{tikzpicture}
+	
+	\node[obs]  (s0) {$s_0$};
+	\node[obs, right= 2.4cm of s0](s1){$s_1$};
+	\node[obs, right= 2.4cm of s1](s2){$s_2$};
+
+        \node[emptynode, below= 2cm of s0](x0){};
+	\node[emptynode, below= 2cm of s1](x1){};
+	\node[emptynode, below= 2cm of s2](x2){};
+        
+	\node[latent, below= 3cm of s0](b0){$b_0$};
+	\node[latent, below= 3cm of s1](b1){$b_1$};
+	\node[latent, below= 3cm of s2](b2){$b_2$};
+        
+	\node[rect, ,right = 0.8cm of x0](a0){$a_0$};
+	\node[rect, right = 0.8cm of x1](a1){$a_1$};
+
+	\node[diam, above = 0.6 of a0](r0){$r_0$};
+	\node[diam, above = 0.6 of a1](r1){$r_1$};
+
+         %\node[emptynode, right= 2cm of s2](e1){$\cdots$};
+	%\node[emptynode, right= 2cm of b2](e2){$\cdots$};
+	
+	\edge{s0}{b0,r0};
+	\edge{b0}{b1,a0};
+	\edge{b1}{b2};
+	\edge{a0}{r0};
+	\edge{s1}{r1,b1};
+	\edge{b1}{b2, a1};
+	\edge{s2}{b2};
+	\edge{r1,a1}{b2};
+	\edge{a1}{r1};
+	\edge{a0,r0}{b1};
+
+        %\edge{s2}{e1};
+	%\edge{b2}{e2};
+    \node[draw=black,thin,fit=(s0)(s1)(s2), rectangle,inner sep=10pt](rect1)  {};
+%    \node[const, above = 2.9cm of a1](){environment};
+    \node[draw=black,thin,fit=(b0)(b1)(b2), rectangle,inner sep=10pt](rect1)  {};
+%    \node[const, below = 1.9cm of a1](){agent};
+
+\end{tikzpicture}
+\end{document}
diff --git a/tikz/bandits3.pdf b/tikz/bandits3.pdf
new file mode 100644
index 0000000000..00f7d235d4
Binary files /dev/null and b/tikz/bandits3.pdf differ
diff --git a/tikz/bandits3.tex b/tikz/bandits3.tex
new file mode 100644
index 0000000000..a3f2505526
--- /dev/null
+++ b/tikz/bandits3.tex
@@ -0,0 +1,55 @@
+\documentclass[tikz, border=1cm]{standalone}
+% main document, called main.tex
+\usepackage{tikz}
+\usetikzlibrary{bayesnet}
+
+\begin{document}
+% \title{sequential_prediction}
+% \author{Dhruv Patel}
+% \date{July 2022}
+\tikzstyle{emptynode} = [rectangle, draw=black!0, minimum size=3mm]
+\tikzstyle{rect} = [rectangle, draw=black!90, minimum size=3mm]
+\tikzstyle{diam} = [diamond, draw=black!90, minimum width=1.5mm, minimum height = 1.5mm]
+\begin{tikzpicture}
+	
+	\node[latent]  (s0) {$s_0$};
+	\node[latent, right= 2.4cm of s0](s1){$s_1$};
+	\node[latent, right= 2.4cm of s1](s2){$s_2$};
+
+	\node[obs, below= 2cm of s0](x0){$s_0$};
+	\node[obs, below= 2cm of s1](x1){$s_1$};
+	\node[obs, below= 2cm of s2](x2){$s_2$};
+
+	\node[latent, below= 1cm of x0](b0){$b_0$};
+	\node[latent, below= 1cm of x1](b1){$b_1$};
+	\node[latent, below= 1cm of x2](b2){$b_2$};
+        
+	\node[rect, right = 0.8cm of x0](a0){$a_0$};
+	\node[rect, right = 0.8cm of x1](a1){$a_1$};
+        
+	\node[diam, above = 0.6 of a0](r0){$r_0$};
+	\node[diam, above = 0.6 of a1](r1){$r_1$};
+
+        \node[emptynode, right= 2cm of s2](e1){$\cdots$};
+	\node[emptynode, right= 2cm of b2](e2){$\cdots$};
+	
+	\edge{s0}{x0,r0};
+	\edge{x0}{b0};
+	\edge{b0}{b1,a0};
+	\edge{b1}{b2};
+	\edge{a0}{r0,s1};
+	\edge{s1}{r1, x1};
+	\edge{b1}{b2, a1};
+	\edge{s2}{x2};
+	\edge{x2,r1,a1}{b2};
+	\edge{a1}{r1,s2};
+	\edge{a0,r0,x1}{b1};
+%	\edge{s2}{e1};
+	\edge{b2}{e2};
+    \node[draw=black,thin,fit=(s0)(s1)(s2), rectangle,inner sep=10pt](rect1)  {};
+    \node[const, above = 2.9cm of a1](){environment};
+    \node[draw=black,thin,fit=(b0)(b1)(b2), rectangle,inner sep=10pt](rect1)  {};
+    \node[const, below = 1.9cm of a1](){agent};
+
+\end{tikzpicture}
+\end{document}
diff --git a/tikz/pomdp_prediction.pdf b/tikz/pomdp_prediction.pdf
new file mode 100644
index 0000000000..cfd12a14e8
Binary files /dev/null and b/tikz/pomdp_prediction.pdf differ
diff --git a/tikz/pomdp_prediction.tex b/tikz/pomdp_prediction.tex
new file mode 100644
index 0000000000..719c8900e2
--- /dev/null
+++ b/tikz/pomdp_prediction.tex
@@ -0,0 +1,33 @@
+\documentclass[tikz, border=1cm]{standalone}
+% main document, called main.tex
+\usepackage{tikz}
+\usetikzlibrary{bayesnet}
+
+\begin{document}
+% \title{sequential_prediction}
+% \author{Dhruv Patel}
+% \date{July 2022}
+\tikzstyle{emptynode} = [rectangle, draw=black!0, minimum size=3mm]
+\tikzstyle{rect} = [rectangle, draw=black!90, minimum size=3mm]
+\tikzstyle{diam} = [diamond, draw=black!90, minimum width=1.5mm, minimum height = 1.5mm]
+\begin{tikzpicture}
+	
+	\node[latent]  (s0) {$h=(D,x,y)$};
+
+	\node[obs, below= 2cm of s0](x0){$o=(D,x)$};
+
+	\node[latent, below= 1cm of x0](b0){$b=p(\theta|D)$};
+        
+	\node[rect, right = 0.8cm of x0](a0){$a=\hat{y}$};
+        
+	\node[diam, above = 0.6 of a0](r0){$\ell(y,\hat{y}$};
+
+	
+	\edge{s0}{x0, r0};
+	\edge{x0}{b0};
+	\edge{b0}{a0};
+	\edge{a0}{r0};
+
+	
+\end{tikzpicture}
+\end{document}