{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayes Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from filters import bayes\n",
    "from plots import plot_bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discrete Bayes Filter\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Belief vs. Prior\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "belief = [0, 0, 0.4, 0.6, 0, 0, 0, 0, 0, 0]\n",
    "prior = bayes.prior_with_jitter(belief, 2, 0.8, 0.1, 0.1)\n",
    "plot_bayes.plot_belief_prior(belief, prior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cF3+Omz5wdaAmvlEXb9TEW0+vib/HbukF9lFRUUpLS5PD4fAYdzgcHpeFXUpNTY0GDRpkZdcAAAAAehhLz6xIUn5+vmbPnq309HRlZGSorKxMdXV1ysnJkSQVFhbq2LFj2rRpkySpuLhYw4cP14033qjW1lZt2bJF27Zt07Zt27r3SAAAAACEFMthZdasWWpqatKyZctUX1+v1NRUVVRUKCkpSZJUX1/v8Zkrra2tevTRR3Xs2DH16tVLN954o37961/rjjvu6L6jAAAAABByLIcVScrNzVVubq7Pr23YsMHj/uLFi7V48eJAdgMAAACgB7P8oZAAAAAAcCUQVgAAAAAYibACAAAAwEiEFQAAAABGIqwAAAAAMBJhBQAAAICRCCsAAAAAjERYAQAAAGAkwgoAAAAAIxFWAAAAABiJsAIAAADASIQVAAAAAEYirAAAAAAwEmEFAAAAgJEIKwAAAACMRFgBAAAAYKSIYC8AAAAAvg0v+PUV3d+Rp++8ovsDLoVnVgAAAAAYibACAAAAwEiEFQAAAABGIqwAAAAAMBJhBQAAAICRCCsAAAAAjERYAQAAAGAkwgoAAAAAIxFWAAAAABiJsAIAAADASIQVAAAAAEYirAAAAAAwEmEFAAAAgJEIKwAAAACMRFgBAAAAYCTCCgAAAAAjEVYAAAAAGCmgsFJSUqLk5GRFR0crLS1NlZWVfs3bt2+fIiIiNGbMmEB2CwAAAKAHsRxWysvLlZeXpyVLlqimpkYTJ07UtGnTVFdX1+m85uZmzZkzR1OmTAl4sQAAAAB6DsthZfXq1Zo7d67mzZunlJQUFRcXKzExUaWlpZ3Omz9/vrKzs5WRkRHwYgEAAAD0HBFWNm5tbVV1dbUKCgo8xrOyslRVVXXReevXr9f//d//acuWLVq+fPkl9+N0OuV0Ojvut7S0SJJcLpdcLpeVJXfZ+f1d6f2aLNRrYg93W58T5vb414pQrWOonyeBoi7+HTt9wGzUxLfLUZdAelJXdPf3lHPFGzU5x9/jt7ndbr9/Co4fP64hQ4Zo3759yszM7BhfuXKlNm7cqEOHDnnN+d///V/ddtttqqys1HXXXaeioiK98sorqq2tveh+ioqKtHTpUq/xrVu3KiYmxt/lAgAMdObMGWVnZ6u5uVmxsbE+t6EPAEBo86cXSBafWTnPZrN53He73V5jktTW1qbs7GwtXbpU1113nd+PX1hYqPz8/I77LS0tSkxMVFZWVqcHczm4XC45HA5NnTpVkZGRV3Tfpgr1mqQW7bQ8xx7m1lPp7XrinTA5271/FjrzXtHtlvd3NQj18yRQ1OXLZ0k6Qx8wGzXx7XLUJZCe1BXd3ZM4V7xRk3P86QWSxbASHx+v8PBwNTQ0eIw3NjYqISHBa/vTp0/rnXfeUU1NjR566CFJUnt7u9xutyIiIvTmm2/qO9/5jtc8u90uu93uNR4ZGRm0b2ow922qUK2Js81a2PCY226zPD8Ua/hVoXqedFVPros/x00fuDpQE9+6sy5d6UmBuFzfT84Vbz29Jv4eu6UX2EdFRSktLU0Oh8Nj3OFweFwWdl5sbKzeffdd1dbWdtxycnJ0/fXXq7a2VrfeequV3QMAAADoQSxfBpafn6/Zs2crPT1dGRkZKisrU11dnXJyciSde+r+2LFj2rRpk8LCwpSamuoxf8CAAYqOjvYaBwAAAICvshxWZs2apaamJi1btkz19fVKTU1VRUWFkpKSJEn19fWX/MwVAAAAALiUgF5gn5ubq9zcXJ9f27BhQ6dzi4qKVFRUFMhuAQAAAPQglj8UEgAAAACuBMIKAAAAACMRVgAAAAAYibACAAAAwEiEFQAAAABGIqwAAAAAMBJhBQAAAICRCCsAAAAAjERYAQAAAGAkwgoAAAAAIxFWAAAAABiJsAIAAADASIQVAAAAAEYirAAAAAAwEmEFAAAAgJEIKwAAAACMRFgBAAAAYCTCCgAAAAAjEVYAAAAAGImwAgAAAMBIhBUAAAAARiKsAAAAADASYQUAAACAkQgrAAAAAIxEWAEAAABgJMIKAAAAACMRVgAAAAAYibACAAAAwEiEFQAAAABGIqwAAAAAMBJhBQAAAICRCCsAAAAAjERYAQAAAGAkwgoAAAAAIwUUVkpKSpScnKzo6GilpaWpsrLyotvu3btXEyZMUFxcnHr16qVRo0bpX/7lXwJeMAAAAICeIcLqhPLycuXl5amkpEQTJkzQiy++qGnTpunAgQMaNmyY1/a9e/fWQw89pNGjR6t3797au3ev5s+fr969e+sf/uEfuuUgAAAAAIQey2Fl9erVmjt3rubNmydJKi4u1s6dO1VaWqpVq1Z5bT927FiNHTu24/7w4cO1fft2VVZWXjSsOJ1OOZ3OjvstLS2SJJfLJZfLZXXJXXJ+f1d6vyYL9ZrYw93W54S5Pf61IlTrGOrnSaCoi3/HTh8wGzXx7XLUJZCe1BXd/T3lXPFGTc7x9/htbrfb75+C1tZWxcTE6Je//KXuvvvujvGFCxeqtrZWu3fvvuRj1NTUaNq0aVq+fHlH4LlQUVGRli5d6jW+detWxcTE+LtcAICBzpw5o+zsbDU3Nys2NtbnNvQBAAht/vQCyWJYOX78uIYMGaJ9+/YpMzOzY3zlypXauHGjDh06dNG5Q4cO1cmTJ3X27FkVFRXpiSeeuOi2vv6ilpiYqFOnTnV6MJeDy+WSw+HQ1KlTFRkZeUX3bapQr0lq0U7Lc+xhbj2V3q4n3gmTs91mae57Rbdb3t/VINTPk0BRl3O/0+Pj4zttUPQBs1ET3y5HXQLpSV3R3T2Jc8UbNTnHn14gBXAZmCTZbJ7/M+Z2u73GLlRZWanPPvtMv/vd71RQUKBvfOMb+uEPf+hzW7vdLrvd7jUeGRkZtG9qMPdtqlCtibPNWtjwmNtuszw/FGv4VaF6nnRVT66LP8dNH7g6UBPfurMuXelJgbhc30/OFW89vSb+HrulsBIfH6/w8HA1NDR4jDc2NiohIaHTucnJyZKkm266SSdOnFBRUdFFwwoAAAAAWHrr4qioKKWlpcnhcHiMOxwOj8vCLsXtdns8vQ8AAAAAF7J8GVh+fr5mz56t9PR0ZWRkqKysTHV1dcrJyZEkFRYW6tixY9q0aZMk6YUXXtCwYcM0atQoSec+d+W5557Tww8/3I2HAQAAACDUWA4rs2bNUlNTk5YtW6b6+nqlpqaqoqJCSUlJkqT6+nrV1dV1bN/e3q7CwkJ9+OGHioiI0IgRI/T0009r/vz53XcUAAAAAEJOQC+wz83NVW5urs+vbdiwweP+ww8/zLMoAAAAACyz9JoVAAAAALhSCCsAAAAAjERYAQAAAGAkwgoAAAAAIxFWAAAAABiJsAIAAADASIQVAAAAAEYirAAAAAAwEmEFAAAAgJEIKwAAAACMRFgBAAAAYCTCCgAAAAAjEVYAAAAAGImwAgAAAMBIhBUAAAAARiKsAAAAADASYQUAAACAkQgrAAAAAIxEWAEAAABgJMIKAAAAACMRVgAAAAAYibACAAAAwEiEFQAAAABGIqwAAAAAMBJhBQAAAICRCCsAAAAAjERYAQAAAGAkwgoAAAAAIxFWAAAAABiJsAIAAADASIQVAAAAAEYirAAAAAAwEmEFAAAAgJECCislJSVKTk5WdHS00tLSVFlZedFtt2/frqlTp6p///6KjY1VRkaGdu7cGfCCAQAAAPQMlsNKeXm58vLytGTJEtXU1GjixImaNm2a6urqfG6/Z88eTZ06VRUVFaqurtbkyZM1Y8YM1dTUdHnxAAAAAEKX5bCyevVqzZ07V/PmzVNKSoqKi4uVmJio0tJSn9sXFxdr8eLFuvnmmzVy5EitXLlSI0eO1KuvvtrlxQMAAAAIXRFWNm5tbVV1dbUKCgo8xrOyslRVVeXXY7S3t+v06dPq16/fRbdxOp1yOp0d91taWiRJLpdLLpfLypK77Pz+rvR+TRbqNbGHu63PCXN7/GtFqNYx1M+TQFEX/46dPmA2auLb5ahLID2pK7r7e8q54o2anOPv8dvcbrffPwXHjx/XkCFDtG/fPmVmZnaMr1y5Uhs3btShQ4cu+RjPPvusnn76aR08eFADBgzwuU1RUZGWLl3qNb5161bFxMT4u1wAgIHOnDmj7OxsNTc3KzY21uc29AEACG3+9AIpwLBSVVWljIyMjvEVK1Zo8+bNev/99zud//LLL2vevHn61a9+pe9+97sX3c7XX9QSExN16tSpTg/mcnC5XHI4HJo6daoiIyOv6L5NFeo1SS2y/gYQ9jC3nkpv1xPvhMnZbrM0972i2y3v72oQ6udJoKjLud/p8fHxnTYo+oDZqIlvl6MugfSkrujunsS54o2anONPL5AsXgYWHx+v8PBwNTQ0eIw3NjYqISGh07nl5eWaO3eufvnLX3YaVCTJbrfLbrd7jUdGRgbtmxrMfZsqVGvibLMWNjzmttsszw/FGn5VqJ4nXdWT6+LPcdMHrg7UxLfurEtXelIgLtf3k3PFW0+vib/HbukF9lFRUUpLS5PD4fAYdzgcHpeFXejll1/W/fffr61bt+rOO++0sksAAAAAPZSlZ1YkKT8/X7Nnz1Z6eroyMjJUVlamuro65eTkSJIKCwt17Ngxbdq0SdK5oDJnzhw9//zzGj9+fMezMr169VLfvn278VAAAAAAhBLLYWXWrFlqamrSsmXLVF9fr9TUVFVUVCgpKUmSVF9f7/GZKy+++KLOnj2rBQsWaMGCBR3j9913nzZs2ND1IwAAAAAQkiyHFUnKzc1Vbm6uz69dGEB27doVyC4AAAAA9HCWPxQSAAAAAK4EwgoAAAAAIxFWAAAAABiJsAIAAADASIQVAAAAAEYirAAAAAAwEmEFAAAAgJEIKwAAAACMRFgBAAAAYCTCCgAAAAAjEVYAAAAAGImwAgAAAMBIhBUAAAAARiKsAAAAADASYQUAAACAkQgrAAAAAIxEWAEAAABgJMIKAAAAACMRVgAAAAAYibACAAAAwEiEFQAAAABGIqwAAAAAMBJhBQAAAICRCCsAAAAAjERYAQAAAGAkwgoAAAAAIxFWAAAAABiJsAIAAADASIQVAAAAAEYirAAAAAAwEmEFAAAAgJEIKwAAAACMRFgBAAAAYCTCCgAAAAAjBRRWSkpKlJycrOjoaKWlpamysvKi29bX1ys7O1vXX3+9wsLClJeXF+haAQAAAPQglsNKeXm58vLytGTJEtXU1GjixImaNm2a6urqfG7vdDrVv39/LVmyRN/85je7vGAAAAAAPYPlsLJ69WrNnTtX8+bNU0pKioqLi5WYmKjS0lKf2w8fPlzPP/+85syZo759+3Z5wQAAAAB6hggrG7e2tqq6uloFBQUe41lZWaqqquq2RTmdTjmdzo77LS0tkiSXyyWXy9Vt+/HH+f1d6f2aLNRrYg93W58T5vb414pQrWOonyeBoi7+HTt9wGzUxLfLUZdAelJXdPf3lHPFGzU5x9/jt7ndbr9/Co4fP64hQ4Zo3759yszM7BhfuXKlNm7cqEOHDnU6f9KkSRozZoyKi4s73a6oqEhLly71Gt+6datiYmL8XS4AwEBnzpxRdna2mpubFRsb63Mb+gAAhDZ/eoFk8ZmV82w2m8d9t9vtNdYVhYWFys/P77jf0tKixMREZWVldXowl4PL5ZLD4dDUqVMVGRl5RfdtqlCvSWrRTstz7GFuPZXerifeCZOz3drPwntFt1ve39Ug1M+TQFGXL58l6Qx9wGw9oSam9IJA1tEVnfUkU2pytesJPz/+8KcXSBbDSnx8vMLDw9XQ0OAx3tjYqISEBCsP1Sm73S673e41HhkZGbRvajD3bapQrYmzLfDg7Wy3WZ4fijX8qlA9T7qqJ9fFn+OmD1wdQrkmpvSCrqwjEJ19P02pSagI5Z8ff/h77JZeYB8VFaW0tDQ5HA6PcYfD4XFZGAAAAAB0leXLwPLz8zV79mylp6crIyNDZWVlqqurU05OjqRzT90fO3ZMmzZt6phTW1srSfrss8908uRJ1dbWKioqSjfccEP3HAUAAACAkGM5rMyaNUtNTU1atmyZ6uvrlZqaqoqKCiUlJUk69yGQF37mytixYzv+u7q6Wlu3blVSUpKOHDnStdUDAAAACFkBvcA+NzdXubm5Pr+2YcMGrzELbzgGAAAAAJIC+FBIAAAAALgSCCsAAAAAjERYAQAAAGAkwgoAAAAAIxFWAAAAABiJsAIAAADASIQVAAAAAEYirAAAAAAwEmEFAAAAgJEIKwAAAACMRFgBAAAAYCTCCgAAAAAjEVYAAAAAGImwAgAAAMBIhBUAAAAARooI9gKupOEFv7Y8xx7u1jO3SKlFO+Vss1mae+TpO7ttHV1xsXVI5tQk0LUEqrN1mORqPldC4TyRro6fH5NqYjpTvmeBrqUr+Pn2djWfyz2dKf/PwO+Uy49nVgAAAAAYibACAAAAwEiEFQAAAABGIqwAAAAAMBJhBQAAAICRCCsAAAAAjERYAQAAAGAkwgoAAAAAIxFWAAAAABiJsAIAAADASIQVAAAAAEYirAAAAAAwEmEFAAAAgJEIKwAAAACMRFgBAAAAYCTCCgAAAAAjEVYAAAAAGCmgsFJSUqLk5GRFR0crLS1NlZWVnW6/e/dupaWlKTo6Wtdee63Wrl0b0GIBAAAA9ByWw0p5ebny8vK0ZMkS1dTUaOLEiZo2bZrq6up8bv/hhx/qjjvu0MSJE1VTU6Of/vSneuSRR7Rt27YuLx4AAABA6LIcVlavXq25c+dq3rx5SklJUXFxsRITE1VaWupz+7Vr12rYsGEqLi5WSkqK5s2bpwcffFDPPfdclxcPAAAAIHRFWNm4tbVV1dXVKigo8BjPyspSVVWVzzn79+9XVlaWx9jtt9+ul156SS6XS5GRkV5znE6nnE5nx/3m5mZJ0ieffCKXy2VlyR4izn5ufU67W2fOtCvCFaa2dpuluU1NTd22jq642Dokc2oS6FoCRU186861UJOLzAnx3yn+OH36tCTJ7XZfdJtQ7wOBrqUr+Pn2xs+3/+uQzKlJoGsJFDXx7Ur0gvMb+O3YsWNuSe59+/Z5jK9YscJ93XXX+ZwzcuRI94oVKzzG9u3b55bkPn78uM85Tz75pFsSN27cuHEL4dtHH3100X5DH+DGjRu3nnHrrBe43W63pWdWzrPZPFOg2+32GrvU9r7GzyssLFR+fn7H/fb2dn3yySeKi4vrdD+XQ0tLixITE/XRRx8pNjb2iu7bVNTEGzXxRk18oy7nesDp06c1ePDgi25DHzAbNfGNunijJt6oyTn+9ALJ4mVg8fHxCg8PV0NDg8d4Y2OjEhISfM4ZOHCgz+0jIiIUFxfnc47dbpfdbvcYu+aaa6wstdvFxsb26BPKF2rijZp4oya+9fS69O3bt9Ov0weuDtTEN+rijZp4oyaX7gWSxRfYR0VFKS0tTQ6Hw2Pc4XAoMzPT55yMjAyv7d98802lp6f7fL0KAAAAAEgBvBtYfn6+fv7zn2vdunU6ePCgFi1apLq6OuXk5Eg699T9nDlzOrbPycnR0aNHlZ+fr4MHD2rdunV66aWX9Oijj3bfUQAAAAAIOZZfszJr1iw1NTVp2bJlqq+vV2pqqioqKpSUlCRJqq+v9/jMleTkZFVUVGjRokV64YUXNHjwYP3rv/6r7rnnnu47isvIbrfrySef9LocoSejJt6oiTdq4ht1ufrwPfNGTXyjLt6oiTdqYo3N7b7U+4UBAAAAwJVn+TIwAAAAALgSCCsAAAAAjERYAQAAAGAkwgoAAAAAIxFWAAAAABiJsNKJkpISJScnKzo6WmlpaaqsrAz2koJm1apVuvnmm9WnTx8NGDBAM2fO1KFDh4K9LKOsWrVKNptNeXl5wV5K0B07dkw//vGPFRcXp5iYGI0ZM0bV1dXBXlbQnD17Vo8//riSk5PVq1cvXXvttVq2bJna29uDvTT4gV7wJXrBpdELvkQv8EQvCAxh5SLKy8uVl5enJUuWqKamRhMnTtS0adM8PkOmJ9m9e7cWLFig3/3ud3I4HDp79qyysrL0+eefB3tpRnj77bdVVlam0aNHB3spQfeXv/xFEyZMUGRkpF5//XUdOHBA//zP/6xrrrkm2EsLmn/6p3/S2rVrtWbNGh08eFDPPPOMnn32Wf3bv/1bsJeGS6AXeKIXdI5e8CV6gTd6QWD4nJWLuPXWWzVu3DiVlpZ2jKWkpGjmzJlatWpVEFdmhpMnT2rAgAHavXu3vvWtbwV7OUH12Wefady4cSopKdHy5cs1ZswYFRcXB3tZQVNQUKB9+/b16L8+X2j69OlKSEjQSy+91DF2zz33KCYmRps3bw7iynAp9ILO0Qu+RC/wRC/wRi8IDM+s+NDa2qrq6mplZWV5jGdlZamqqipIqzJLc3OzJKlfv35BXknwLViwQHfeeae++93vBnspRtixY4fS09P1/e9/XwMGDNDYsWP17//+78FeVlDddttteuutt3T48GFJ0v/8z/9o7969uuOOO4K8MnSGXnBp9IIv0Qs80Qu80QsCExHsBZjo1KlTamtrU0JCgsd4QkKCGhoagrQqc7jdbuXn5+u2225TampqsJcTVL/4xS/0hz/8QW+//Xawl2KMDz74QKWlpcrPz9dPf/pT/f73v9cjjzwiu92uOXPmBHt5QfGTn/xEzc3NGjVqlMLDw9XW1qYVK1bohz/8YbCXhk7QCzpHL/gSvcAbvcAbvSAwhJVO2Gw2j/tut9trrCd66KGH9Mc//lF79+4N9lKC6qOPPtLChQv15ptvKjo6OtjLMUZ7e7vS09O1cuVKSdLYsWP1pz/9SaWlpT22QZWXl2vLli3aunWrbrzxRtXW1iovL0+DBw/WfffdF+zl4RLoBb7RC86hF/hGL/BGLwgMYcWH+Ph4hYeHe/3lrLGx0esvbD3Nww8/rB07dmjPnj0aOnRosJcTVNXV1WpsbFRaWlrHWFtbm/bs2aM1a9bI6XQqPDw8iCsMjkGDBumGG27wGEtJSdG2bduCtKLge+yxx1RQUKAf/OAHkqSbbrpJR48e1apVq2hQBqMXXBy94Ev0At/oBd7oBYHhNSs+REVFKS0tTQ6Hw2Pc4XAoMzMzSKsKLrfbrYceekjbt2/Xb37zGyUnJwd7SUE3ZcoUvfvuu6qtre24paen60c/+pFqa2t7ZHOSpAkTJni9lenhw4eVlJQUpBUF35kzZxQW5vnrNjw8nLerNBy9wBu9wBu9wDd6gTd6QWB4ZuUi8vPzNXv2bKWnpysjI0NlZWWqq6tTTk5OsJcWFAsWLNDWrVv1q1/9Sn369On4S2Pfvn3Vq1evIK8uOPr06eN1nXbv3r0VFxfXo6/fXrRokTIzM7Vy5Urde++9+v3vf6+ysjKVlZUFe2lBM2PGDK1YsULDhg3TjTfeqJqaGq1evVoPPvhgsJeGS6AXeKIXeKMX+EYv8EYvCJAbF/XCCy+4k5KS3FFRUe5x48a5d+/eHewlBY0kn7f169cHe2lG+fa3v+1euHBhsJcRdK+++qo7NTXVbbfb3aNGjXKXlZUFe0lB1dLS4l64cKF72LBh7ujoaPe1117rXrJkidvpdAZ7afADveBL9AL/0AvOoRd4ohcEhs9ZAQAAAGAkXrMCAAAAwEiEFQAAAABGIqwAAAAAMBJhBQAAAICRCCsAAAAAjERYAQAAAGAkwgoAAAAAIxFWAAAAABiJsAIAAADASIQVAAAAAEYirAAAAAAw0v8D6BNw6vX73NMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "belief = [0.05, 0.05, 0.05, 0.05, 0.55, 0.05, 0.05, 0.05, 0.05, 0.05]\n",
    "prior = bayes.prior_by_convol(belief, offset=1, kernel=[0.1, 0.8, 0.1])\n",
    "plot_bayes.plot_belief_prior(belief, prior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prior vs. Posterior\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])\n",
    "prior = np.array([0.1] * 10)\n",
    "likelihood = bayes.likelihood(data, z=1, z_prob=0.75)\n",
    "posterior = bayes.posterior(likelihood, prior)\n",
    "plot_bayes.plot_prior_posterior(prior, posterior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kernel = (0.1, 0.8, 0.1)\n",
    "prior = bayes.prior_by_convol(posterior, 1, kernel)\n",
    "likelihood = bayes.likelihood(data, z=1, z_prob=0.75)\n",
    "posterior = bayes.posterior(likelihood, prior)\n",
    "plot_bayes.plot_prior_posterior(prior, posterior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prior = bayes.prior_by_convol(posterior, 1, kernel)\n",
    "likelihood = bayes.likelihood(data, z=0, z_prob=0.75)\n",
    "posterior = bayes.posterior(likelihood, prior)\n",
    "plot_bayes.plot_prior_posterior(prior, posterior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prior = bayes.prior_by_convol(posterior, 1, kernel)\n",
    "likelihood = bayes.likelihood(data, z=0, z_prob=0.75)\n",
    "posterior = bayes.posterior(likelihood, prior)\n",
    "plot_bayes.plot_prior_posterior(prior, posterior, ylim=(0, 0.6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "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": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}