Bayes_Rules/Chapter 7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 7 MCMC under the Hood\n",
    "\n",
    "## 7.1 The big idea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc as pm\n",
    "import pyreadr\n",
    "import requests\n",
    "import seaborn as sns\n",
    "\n",
    "from plotnine import (\n",
    "    aes,\n",
    "    after_stat,\n",
    "    geom_histogram,\n",
    "    geom_line,\n",
    "    ggplot,\n",
    "    labs,\n",
    "    stat_function,\n",
    ")\n",
    "from scipy.stats import beta, binom, norm, uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"%config InlineBackend.figure_format = 'retina'\\n%load_ext watermark\\n%load_ext nb_black\\nRANDOM_SEED = 1301\\nnp.random.seed(RANDOM_SEED)\\nrng = np.random.default_rng(RANDOM_SEED)\\naz.style.use(\\\"arviz-darkgrid\\\")\";\n",
       "                var nbb_formatted_code = \"%config InlineBackend.figure_format = 'retina'\\n%load_ext watermark\\n%load_ext nb_black\\nRANDOM_SEED = 1301\\nnp.random.seed(RANDOM_SEED)\\nrng = np.random.default_rng(RANDOM_SEED)\\naz.style.use(\\\"arviz-darkgrid\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%config InlineBackend.figure_format = 'retina'\n",
    "%load_ext watermark\n",
    "%load_ext nb_black\n",
    "RANDOM_SEED = 1301\n",
    "np.random.seed(RANDOM_SEED)\n",
    "rng = np.random.default_rng(RANDOM_SEED)\n",
    "az.style.use(\"arviz-darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tomkealy/opt/anaconda3/envs/bayes_rules/lib/python3.10/site-packages/plotnine/geoms/geom_path.py:75: PlotnineWarning: geom_path: Removed 2 rows containing missing values.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (377505111)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"mc_tour = pd.DataFrame(data={\\\"mu\\\": np.random.normal(4, 0.6, 5000)})\\n\\n(\\n    ggplot(mc_tour, aes(x=mc_tour[\\\"mu\\\"]))\\n    + geom_histogram(aes(y=after_stat(\\\"density\\\")), color=\\\"white\\\", bins=15)\\n    + stat_function(fun=norm.ppf, args=4, color=\\\"blue\\\")\\n)\";\n",
       "                var nbb_formatted_code = \"mc_tour = pd.DataFrame(data={\\\"mu\\\": np.random.normal(4, 0.6, 5000)})\\n\\n(\\n    ggplot(mc_tour, aes(x=mc_tour[\\\"mu\\\"]))\\n    + geom_histogram(aes(y=after_stat(\\\"density\\\")), color=\\\"white\\\", bins=15)\\n    + stat_function(fun=norm.ppf, args=4, color=\\\"blue\\\")\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mc_tour = pd.DataFrame(data={\"mu\": np.random.normal(4, 0.6, 5000)})\n",
    "\n",
    "(\n",
    "    ggplot(mc_tour, aes(x=mc_tour[\"mu\"]))\n",
    "    + geom_histogram(aes(y=after_stat(\"density\")), color=\"white\", bins=15)\n",
    "    + stat_function(fun=norm.ppf, args=4, color=\"blue\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2 The Metropolis-Hastings algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.060225707987863"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"current = 3\\nproposal = rng.uniform(low=current - 1, high=current + 1)\\nproposal\";\n",
       "                var nbb_formatted_code = \"current = 3\\nproposal = rng.uniform(low=current - 1, high=current + 1)\\nproposal\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "current = 3\n",
    "proposal = rng.uniform(low=current - 1, high=current + 1)\n",
    "proposal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.250620045181512e-09"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"proposal_plaus = norm.pdf(proposal, 0, 1) * norm.pdf(6.25, proposal, 0.75)\\nproposal_plaus\";\n",
       "                var nbb_formatted_code = \"proposal_plaus = norm.pdf(proposal, 0, 1) * norm.pdf(6.25, proposal, 0.75)\\nproposal_plaus\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "proposal_plaus = norm.pdf(proposal, 0, 1) * norm.pdf(6.25, proposal, 0.75)\n",
    "proposal_plaus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9719280468414924e-07"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"current_plaus = norm.pdf(current, 0, 1) * norm.pdf(6.25, current, 0.75)\\ncurrent_plaus\";\n",
       "                var nbb_formatted_code = \"current_plaus = norm.pdf(current, 0, 1) * norm.pdf(6.25, current, 0.75)\\ncurrent_plaus\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "current_plaus = norm.pdf(current, 0, 1) * norm.pdf(6.25, current, 0.75)\n",
    "current_plaus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.021555654893138123"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"alpha = min(1, proposal_plaus / current_plaus)\\nalpha\";\n",
       "                var nbb_formatted_code = \"alpha = min(1, proposal_plaus / current_plaus)\\nalpha\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = min(1, proposal_plaus / current_plaus)\n",
    "alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"np.random.choice([proposal, current], size=1, replace=True, p=[alpha, 1 - alpha])\";\n",
       "                var nbb_formatted_code = \"np.random.choice([proposal, current], size=1, replace=True, p=[alpha, 1 - alpha])\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "np.random.choice([proposal, current], size=1, replace=True, p=[alpha, 1 - alpha])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"def one_mh_iteration(w, current):\\n    proposal = rng.uniform(low=current - w, high=current + w)\\n    proposal_plaus = norm.pdf(proposal, 0, 1) * norm.pdf(6.25, proposal, 0.75)\\n    current_plaus = norm.pdf(current, 0, 1) * norm.pdf(6.25, current, 0.75)\\n    alpha = min(1, proposal_plaus / current_plaus)\\n    next_stop = np.random.choice([proposal, current], size=1, replace=True, p=[alpha, 1 - alpha])\\n    data = {\\\"proposal\\\": proposal, \\\"alpha\\\": alpha, \\\"next_stop\\\": next_stop}\\n    return pd.DataFrame(data)\";\n",
       "                var nbb_formatted_code = \"def one_mh_iteration(w, current):\\n    proposal = rng.uniform(low=current - w, high=current + w)\\n    proposal_plaus = norm.pdf(proposal, 0, 1) * norm.pdf(6.25, proposal, 0.75)\\n    current_plaus = norm.pdf(current, 0, 1) * norm.pdf(6.25, current, 0.75)\\n    alpha = min(1, proposal_plaus / current_plaus)\\n    next_stop = np.random.choice(\\n        [proposal, current], size=1, replace=True, p=[alpha, 1 - alpha]\\n    )\\n    data = {\\\"proposal\\\": proposal, \\\"alpha\\\": alpha, \\\"next_stop\\\": next_stop}\\n    return pd.DataFrame(data)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def one_mh_iteration(w, current):\n",
    "    proposal = rng.uniform(low=current - w, high=current + w)\n",
    "    proposal_plaus = norm.pdf(proposal, 0, 1) * norm.pdf(6.25, proposal, 0.75)\n",
    "    current_plaus = norm.pdf(current, 0, 1) * norm.pdf(6.25, current, 0.75)\n",
    "    alpha = min(1, proposal_plaus / current_plaus)\n",
    "    next_stop = np.random.choice(\n",
    "        [proposal, current], size=1, replace=True, p=[alpha, 1 - alpha]\n",
    "    )\n",
    "    data = {\"proposal\": proposal, \"alpha\": alpha, \"next_stop\": next_stop}\n",
    "    return pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>proposal</th>\n",
       "      <th>alpha</th>\n",
       "      <th>next_stop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.707278</td>\n",
       "      <td>1</td>\n",
       "      <td>3.707278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   proposal  alpha  next_stop\n",
       "0  3.707278      1   3.707278"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"one_mh_iteration(w=1, current=3)\";\n",
       "                var nbb_formatted_code = \"one_mh_iteration(w=1, current=3)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
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       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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   "source": [
    "one_mh_iteration(w=1, current=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3 Implementing the Metropolis-Hastings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"def mh_tour(N, w):\\n    current = 3\\n    mu = np.zeros(N)\\n    for step in range(N):\\n        sim = one_mh_iteration(w=w, current=current)\\n        mu[step] = sim[\\\"next_stop\\\"]\\n        current = sim[\\\"next_stop\\\"][0]\\n    return pd.DataFrame(mu, columns=[\\\"mu\\\"])\";\n",
       "                var nbb_formatted_code = \"def mh_tour(N, w):\\n    current = 3\\n    mu = np.zeros(N)\\n    for step in range(N):\\n        sim = one_mh_iteration(w=w, current=current)\\n        mu[step] = sim[\\\"next_stop\\\"]\\n        current = sim[\\\"next_stop\\\"][0]\\n    return pd.DataFrame(mu, columns=[\\\"mu\\\"])\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def mh_tour(N, w):\n",
    "    current = 3\n",
    "    mu = np.zeros(N)\n",
    "    for step in range(N):\n",
    "        sim = one_mh_iteration(w=w, current=current)\n",
    "        mu[step] = sim[\"next_stop\"]\n",
    "        current = sim[\"next_stop\"][0]\n",
    "    return pd.DataFrame(mu, columns=[\"mu\"])"
   ]
  },
  {
   "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>mu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.787656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.463304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mu\n",
       "0  3.000000\n",
       "1  3.000000\n",
       "2  3.000000\n",
       "3  3.787656\n",
       "4  4.463304"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"mh_tour(N=5, w=1)\";\n",
       "                var nbb_formatted_code = \"mh_tour(N=5, w=1)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
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       "<IPython.core.display.Javascript object>"
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     "metadata": {},
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   "source": [
    "mh_tour(N=5, w=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"mh_simulation = mh_tour(N=5000, w=1)\";\n",
       "                var nbb_formatted_code = \"mh_simulation = mh_tour(N=5000, w=1)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     "metadata": {},
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   ],
   "source": [
    "mh_simulation = mh_tour(N=5000, w=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (280038307)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"ggplot(mh_simulation, aes(x=mh_simulation.index, y=mh_simulation[\\\"mu\\\"])) + geom_line()\";\n",
       "                var nbb_formatted_code = \"ggplot(mh_simulation, aes(x=mh_simulation.index, y=mh_simulation[\\\"mu\\\"])) + geom_line()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(mh_simulation, aes(x=mh_simulation.index, y=mh_simulation[\"mu\"])) + geom_line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (280037443)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"ggplot(mh_simulation, aes(x=mh_simulation[\\\"mu\\\"])) + geom_histogram(\\n    aes(y=after_stat(\\\"density\\\")), color=\\\"white\\\", bins=20\\n)  # + stat_function(fun = norm.pdf, args = np.array([4,0.6]), color = \\\"blue\\\")\";\n",
       "                var nbb_formatted_code = \"ggplot(mh_simulation, aes(x=mh_simulation[\\\"mu\\\"])) + geom_histogram(\\n    aes(y=after_stat(\\\"density\\\")), color=\\\"white\\\", bins=20\\n)  # + stat_function(fun = norm.pdf, args = np.array([4,0.6]), color = \\\"blue\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(mh_simulation, aes(x=mh_simulation[\"mu\"])) + geom_histogram(\n",
    "    aes(y=after_stat(\"density\")), color=\"white\", bins=20\n",
    ")  # + stat_function(fun = norm.pdf, args = np.array([4,0.6]), color = \"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.4 Tuning the Metropolis-Hastings algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (280099636)>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"mh_simulation = mh_tour(N=5000, w=0.01)\\nggplot(mh_simulation, aes(x=mh_simulation.index, y=mh_simulation[\\\"mu\\\"])) + geom_line()\";\n",
       "                var nbb_formatted_code = \"mh_simulation = mh_tour(N=5000, w=0.01)\\nggplot(mh_simulation, aes(x=mh_simulation.index, y=mh_simulation[\\\"mu\\\"])) + geom_line()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mh_simulation = mh_tour(N=5000, w=0.01)\n",
    "ggplot(mh_simulation, aes(x=mh_simulation.index, y=mh_simulation[\"mu\"])) + geom_line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (280032296)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"mh_simulation = mh_tour(N=5000, w=100)\\nggplot(mh_simulation, aes(x=mh_simulation.index, y=mh_simulation[\\\"mu\\\"])) + geom_line()\";\n",
       "                var nbb_formatted_code = \"mh_simulation = mh_tour(N=5000, w=100)\\nggplot(mh_simulation, aes(x=mh_simulation.index, y=mh_simulation[\\\"mu\\\"])) + geom_line()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mh_simulation = mh_tour(N=5000, w=100)\n",
    "ggplot(mh_simulation, aes(x=mh_simulation.index, y=mh_simulation[\"mu\"])) + geom_line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.5 A Beta-Binomial example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"def one_beta_binom_iteration(a, b, current):\\n    proposal = rng.beta(a, b)\\n    proposal_plaus = beta.pdf(proposal, 2, 3) * binom.pmf(1, 2, proposal)\\n    proposal_q = beta.pdf(proposal, a, b)\\n    current_plaus = beta.pdf(current, 2, 3) * binom.pmf(1, 2, current)\\n    current_q = beta.pdf(current, a, b)\\n    alpha = min(1, proposal_plaus / current_plaus * current_q / proposal_q)\\n    next_stop = np.random.choice([proposal, current], size=1, replace=True, p=[alpha, 1 - alpha])\\n    data = {\\\"proposal\\\": proposal, \\\"alpha\\\": alpha, \\\"next_stop\\\": next_stop}\\n    return pd.DataFrame(data)\";\n",
       "                var nbb_formatted_code = \"def one_beta_binom_iteration(a, b, current):\\n    proposal = rng.beta(a, b)\\n    proposal_plaus = beta.pdf(proposal, 2, 3) * binom.pmf(1, 2, proposal)\\n    proposal_q = beta.pdf(proposal, a, b)\\n    current_plaus = beta.pdf(current, 2, 3) * binom.pmf(1, 2, current)\\n    current_q = beta.pdf(current, a, b)\\n    alpha = min(1, proposal_plaus / current_plaus * current_q / proposal_q)\\n    next_stop = np.random.choice(\\n        [proposal, current], size=1, replace=True, p=[alpha, 1 - alpha]\\n    )\\n    data = {\\\"proposal\\\": proposal, \\\"alpha\\\": alpha, \\\"next_stop\\\": next_stop}\\n    return pd.DataFrame(data)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def one_beta_binom_iteration(a, b, current):\n",
    "    proposal = rng.beta(a, b)\n",
    "    proposal_plaus = beta.pdf(proposal, 2, 3) * binom.pmf(1, 2, proposal)\n",
    "    proposal_q = beta.pdf(proposal, a, b)\n",
    "    current_plaus = beta.pdf(current, 2, 3) * binom.pmf(1, 2, current)\n",
    "    current_q = beta.pdf(current, a, b)\n",
    "    alpha = min(1, proposal_plaus / current_plaus * current_q / proposal_q)\n",
    "    next_stop = np.random.choice(\n",
    "        [proposal, current], size=1, replace=True, p=[alpha, 1 - alpha]\n",
    "    )\n",
    "    data = {\"proposal\": proposal, \"alpha\": alpha, \"next_stop\": next_stop}\n",
    "    return pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"def beta_binom_tour(N, a, b):\\n    current = 0.5\\n    mu = np.zeros(N)\\n    for step in range(N):\\n        sim = one_beta_binom_iteration(a=a, b=b, current=current)\\n        mu[step] = sim[\\\"next_stop\\\"]\\n        current = sim[\\\"next_stop\\\"][0]\\n    return pd.DataFrame(mu, columns=[\\\"mu\\\"])\";\n",
       "                var nbb_formatted_code = \"def beta_binom_tour(N, a, b):\\n    current = 0.5\\n    mu = np.zeros(N)\\n    for step in range(N):\\n        sim = one_beta_binom_iteration(a=a, b=b, current=current)\\n        mu[step] = sim[\\\"next_stop\\\"]\\n        current = sim[\\\"next_stop\\\"][0]\\n    return pd.DataFrame(mu, columns=[\\\"mu\\\"])\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def beta_binom_tour(N, a, b):\n",
    "    current = 0.5\n",
    "    mu = np.zeros(N)\n",
    "    for step in range(N):\n",
    "        sim = one_beta_binom_iteration(a=a, b=b, current=current)\n",
    "        mu[step] = sim[\"next_stop\"]\n",
    "        current = sim[\"next_stop\"][0]\n",
    "    return pd.DataFrame(mu, columns=[\"mu\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"betabin_sim = beta_binom_tour(N=5000, a=1, b=1)\";\n",
       "                var nbb_formatted_code = \"betabin_sim = beta_binom_tour(N=5000, a=1, b=1)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "betabin_sim = beta_binom_tour(N=5000, a=1, b=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (379518913)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"ggplot(betabin_sim, aes(x=betabin_sim.index, y=betabin_sim[\\\"mu\\\"])) + geom_line()\";\n",
       "                var nbb_formatted_code = \"ggplot(betabin_sim, aes(x=betabin_sim.index, y=betabin_sim[\\\"mu\\\"])) + geom_line()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(betabin_sim, aes(x=betabin_sim.index, y=betabin_sim[\"mu\"])) + geom_line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tomkealy/opt/anaconda3/envs/bayes_rules/lib/python3.10/site-packages/plotnine/stats/stat_bin.py:95: PlotnineWarning: 'stat_bin()' using 'bins = 32'. Pick better value with 'binwidth'.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (280054619)>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"ggplot(betabin_sim, aes(x=betabin_sim[\\\"mu\\\"])) + geom_histogram(\\n    aes(y=after_stat(\\\"density\\\")), color=\\\"white\\\"\\n)  # +\\n# stat_function(fun = dbeta, args = list(3, 4), color = \\\"blue\\\")\";\n",
       "                var nbb_formatted_code = \"ggplot(betabin_sim, aes(x=betabin_sim[\\\"mu\\\"])) + geom_histogram(\\n    aes(y=after_stat(\\\"density\\\")), color=\\\"white\\\"\\n)  # +\\n# stat_function(fun = dbeta, args = list(3, 4), color = \\\"blue\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(betabin_sim, aes(x=betabin_sim[\"mu\"])) + geom_histogram(\n",
    "    aes(y=after_stat(\"density\")), color=\"white\"\n",
    ")  # +\n",
    "# stat_function(fun = dbeta, args = list(3, 4), color = \"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 2022-06-24T15:36:28.119847+02:00\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.4\n",
      "IPython version      : 8.1.1\n",
      "\n",
      "Compiler    : Clang 12.0.1 \n",
      "OS          : Darwin\n",
      "Release     : 18.7.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 8\n",
      "Architecture: 64bit\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"watermark\";\n",
       "                var nbb_formatted_code = \"watermark\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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