{
 "metadata": {
  "name": "",
  "signature": "sha256:cdbc96283b0e321c568e3856c5a84eb895cda88bca109a3f67b7138527be789c"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "\u5358\u7d14\u306a\u30ac\u30a6\u30b9\u5206\u5e03\u306e\u5e73\u5747\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u63a8\u5b9a"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\u6e96\u5099\u4f5c\u696d"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u307e\u305a\u3044\u308d\u3044\u308d\u5fc5\u8981\u306a\u3082\u306e\u306e import\n",
      "\n",
      "%matplotlib\u306e\u30a4\u30f3\u30e9\u30a4\u30f3\u6307\u5b9a\u3082\u3057\u3066\u304a\u304f"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from pymc import Normal, deterministic, MCMC, Matplot\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\u5e73\u5747\u30d1\u30e9\u30e1\u30fc\u30bf \\\\(\\mu\\\\) \u306e\u4e8b\u524d\u5206\u5e03"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\\\\(x\\\\) \u3092\u751f\u6210\u3059\u308b\u6b63\u898f\u5206\u5e03\u306e\u5e73\u5747\u306f \\\\(\\mu\\\\)\uff0e\u3053\u306e \\\\(\\mu\\\\) \u306e\u4e8b\u524d\u5206\u5e03\u306f\uff0c\u5e73\u5747\u304c 0 \u3067\uff0c\u5206\u6563\u304c \\\\(1/0.01\\\\)\uff0e\n",
      "\u3068\u308a\u3042\u3048\u305a\u3069\u3093\u306a\u611f\u3058\u306e\u5206\u5e03\u306b\u306a\u308b\u304b\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u304a\u304f"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mu = np.random.normal(0.0, 0.1, 100)\n",
      "plt.hist(mu)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "(array([  4.,   6.,   7.,  20.,  19.,  20.,  12.,   7.,   3.,   2.]),\n",
        " array([-0.24981899, -0.19842765, -0.14703632, -0.09564499, -0.04425366,\n",
        "         0.00713767,  0.05852901,  0.10992034,  0.16131167,  0.212703  ,\n",
        "         0.26409433]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b93a6d0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "\u8a13\u7df4\u7528\u30b5\u30f3\u30d7\u30eb\u30c7\u30fc\u30bf\u306e\u751f\u6210"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`x_sample` \u306f\u5b66\u7fd2\u7528\u306e\u30b5\u30f3\u30d7\u30eb\uff0e\n",
      "\u5e73\u5747\u304c 1.0 \u3067\uff0c\u6a19\u6e96\u504f\u5dee\u304c 1.0 \u306e\u6b63\u898f\u5206\u5e03\u306b\u5f93\u3046\uff0e\n",
      "\u307e\u305a\u306f\u5c11\u6570\u306e\u30b5\u30f3\u30d7\u30eb 5 \u500b\u3060\u3051\u3067\u3084\u3063\u3066\u307f\u308b\uff0e\n",
      "\n",
      "\\\\(\\mu\\\\) \u304c 1.0 \u3092\u4e2d\u5fc3\u306b\u5206\u5e03\u3059\u308b\u3088\u3046\u306b\u306a\u308c\u3070\u6210\u529f"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_sample = np.random.normal(loc=1.0, scale=1.0, size=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u4e8b\u5f8c\u78ba\u7387\u306e\u63a8\u5b9a"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u751f\u6210\u30e2\u30c7\u30eb\u306f\uff0c\u975e\u5e38\u306b\u5358\u7d14\uff1a\n",
      "\\\\[\n",
      "\\mu\\sim\\mathrm{Normal}(0, 0.1^2)\n",
      "\\\\]\n",
      "\\\\[\n",
      "x\\sim\\mathrm{Normal}(\\mu, 1.0^2)\n",
      "\\\\]\n",
      "\n",
      "\u203b NumPy \u306e np.random.normal \u306f\u5e73\u5747\u3068\u6a19\u6e96\u504f\u5dee\u3067\u6307\u5b9a\u3059\u308b\u304c\uff0cPyMC \u306e\u6b63\u898f\u5206\u5e03\u306f\uff0c\u6a19\u6e96\u504f\u5dee\u3067\u306f\u306a\u304f\u7cbe\u5ea6\uff08\u5206\u6563\u306e\u9006\u6570\u30fb\u9006\u884c\u5217\uff09\u3067\u6307\u5b9a\u3059\u308b\n",
      "\n",
      "\\\\(x\\\\) \u306f\uff0c\u5e73\u5747\u304c \\\\(\\mu\\\\) \u3067\uff0c\u5206\u6563\u304c \\\\(1/0.01\\\\) \u3068\u3057\u3066\u304a\u304f\uff0e\u3053\u306e \\\\(x\\\\) \u306b\u3064\u3044\u3066\u306f\u6a19\u672c\u304c\u89b3\u6e2c\u3055\u308c\u308b\uff08\u30b0\u30e9\u30d5\u30a3\u30ab\u30eb\u30e2\u30c7\u30eb\u3067\u3044\u3048\u3070\u9ed2\u4e38\uff09\u306e\u3067\uff0c  `value=x_sample` \u3068 `observed=True` \u3092\u6307\u5b9a\u3057\u3066\u304a\u304f\uff0e"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mu = Normal('mu', 0, 1 / (0.1 ** 2))\n",
      "x = Normal('x', mu=mu, tau=1/(1.0**2), value=x_sample, observed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "MCMC\u306b\u3088\u308b\u4e8b\u5f8c\u78ba\u7387\u306e\u63a8\u5b9a"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u30de\u30eb\u30b3\u30d5\u9023\u9396\u306e\u30e2\u30c7\u30eb\u3092\u6307\u5b9a\uff0e\u8d85\u521d\u5fc3\u8005\u306a\u306e\u3067\uff0c\u6700\u9069\u5316\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u610f\u5473\u3068\u304b\u306f\u5206\u304b\u3063\u3066\u306a\u3044\u304c\uff0c\u3068\u308a\u3042\u3048\u305a\uff0c `input` \u306b\u95a2\u9023\u3059\u308b\u5909\u6570\u3092\u5165\u308c\u3066\u304a\u3044\u3066\uff0c\u9069\u5f53\u306a\u56de\u6570\u3060\u3051\u53cd\u5fa9\u3057\u3066\u307f\u308b"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M = MCMC(input=[mu, x])\n",
      "M.sample(iter=10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------97%----------------- ] 9751 of 10000 complete in 0.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------100%-----------------] 10000 of 10000 complete in 0.5 sec"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u5de6\u4e0a\u306f\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u4e2d\u306e \\\\(\\mu\\\\) \u306e\u5024\uff0c\u5de6\u4e0b\u306f\u81ea\u5df1\u76f8\u95a2\u3067\u53ce\u675f\u3057\u3066\u308b\u304b\u3069\u3046\u304b\u3092\u898b\u308b\uff0e\u53f3\u304c \\\\(\\mu\\\\) \u306e\u30c7\u30fc\u30bf\u3092\u89b3\u6e2c\u3057\u305f\u3042\u3068\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u4e8b\u5f8c\u5206\u5e03"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Matplot.plot(mu)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Plotting mu\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TQwXn7VwRkVpScz3XF9M++6RfSAtVSM2v30Gzc4nFTMeojz6anOc3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\nhBBCCCFEaf4FcoMmCBM0vcoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1115a4ad0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\u30b5\u30f3\u30d7\u30eb\u6570\u3092\u5897\u3084\u3057\u305f\u3089"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u4eca\u5ea6\u306f\u540c\u3058\u30e2\u30c7\u30eb\u3067\uff0c\u30b5\u30f3\u30d7\u30eb\u6570\u3092\u30ac\u30fc\u30f3\u30681000\u500b\u307e\u3067\u5897\u3084\u3057\u3066\u307f\u308b\uff0e"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_sample = np.random.normal(loc=1.0, scale=1.0, size=1000)\n",
      "mu = Normal('mu', 0, 1 / (0.1 ** 2))\n",
      "x = Normal('x', mu=mu, tau=1/(1.0**2), value=x_sample, observed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M = MCMC(input=[mu, x])\n",
      "M.sample(iter=10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------76%--------          ] 7610 of 10000 complete in 0.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------100%-----------------] 10000 of 10000 complete in 0.7 sec"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u3059\u308b\u3068 \\\\(\\mu\\\\) \u306e\u4e8b\u5f8c\u5206\u5e03\u304c\u305a\u3063\u30681.0\u306e\u5468\u56f2\u3067\u5c16\u3063\u305f\u5206\u5e03\u306b\u306a\u3063\u3066\u308b\uff0e\u3059\u306a\u308f\u3061\uff0c\u4e8b\u524d\u5206\u5e03\u306e\u5f71\u97ff\u304c\u6e1b\u3063\u3066\u30c7\u30fc\u30bf\u306b\u5408\u308f\u305b\u305f\u5206\u5e03\u306b\u306a\u3063\u3066\u308b\u3053\u3068\u304c\u5206\u304b\u308b\uff0e"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Matplot.plot(mu)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Plotting mu\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JRHJGOU8i5U05TyIiIiKSkoInERERkQwoeBIREZGklPMUppwnEckZ5TyJlDfl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\nMlESwZOSxkUkjsnAsJh112LB0/7Av7x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       "text": [
        "<matplotlib.figure.Figure at 0x1114e6ad0>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}