Gaussian Random Walk.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Pkg.add(\"Distributions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "using Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "using PyPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 1000\n",
    "TRIALS = 10000\n",
    "MU = 0\n",
    "SIGMA = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f08896f41d0>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "([1.0,1.0,0.0,1.0,2.0,0.0,1.0,2.0,1.0,4.0  …  1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0],[-123.824,-121.273,-118.723,-116.173,-113.622,-111.072,-108.521,-105.971,-103.42,-100.87  …  108.269,110.819,113.37,115.92,118.471,121.021,123.571,126.122,128.672,131.223],Any[PyObject <matplotlib.patches.Rectangle object at 0x7f08893bc650>,PyObject <matplotlib.patches.Rectangle object at 0x7f08893bcc10>,PyObject <matplotlib.patches.Rectangle object at 0x7f08893ce2d0>,PyObject <matplotlib.patches.Rectangle object at 0x7f08893ce7d0>,PyObject <matplotlib.patches.Rectangle object at 0x7f08893cee50>,PyObject <matplotlib.patches.Rectangle object at 0x7f088935b510>,PyObject <matplotlib.patches.Rectangle object at 0x7f088935ba10>,PyObject <matplotlib.patches.Rectangle object at 0x7f088935bf10>,PyObject <matplotlib.patches.Rectangle object at 0x7f0889369750>,PyObject <matplotlib.patches.Rectangle object at 0x7f0889369dd0>  …  PyObject <matplotlib.patches.Rectangle object at 0x7f088920b290>,PyObject <matplotlib.patches.Rectangle object at 0x7f088920b910>,PyObject <matplotlib.patches.Rectangle object at 0x7f088920be10>,PyObject <matplotlib.patches.Rectangle object at 0x7f08891994d0>,PyObject <matplotlib.patches.Rectangle object at 0x7f08891999d0>,PyObject <matplotlib.patches.Rectangle object at 0x7f0889199ed0>,PyObject <matplotlib.patches.Rectangle object at 0x7f08891a5710>,PyObject <matplotlib.patches.Rectangle object at 0x7f08891a5c10>,PyObject <matplotlib.patches.Rectangle object at 0x7f08891b2150>,PyObject <matplotlib.patches.Rectangle object at 0x7f08891b2650>])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "for i = 1:TRIALS\n",
    "    mu = MU\n",
    "    for j = 1:N\n",
    "        mu = rand(Normal(mu, SIGMA))\n",
    "    end\n",
    "    push!(data, mu)\n",
    "end\n",
    "\n",
    "figure()\n",
    "title(\"Shifting Normals Marginal Distribution\")\n",
    "plt[:hist](data, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7fcf5e64ce90>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "([1.0,0.0,0.0,0.0,2.0,0.0,1.0,3.0,4.0,2.0  …  6.0,5.0,3.0,2.0,1.0,0.0,0.0,1.0,2.0,2.0],[-114.577,-112.314,-110.05,-107.787,-105.524,-103.261,-100.997,-98.734,-96.4707,-94.2074  …  91.3822,93.6455,95.9088,98.172,100.435,102.699,104.962,107.225,109.488,111.752],Any[PyObject <matplotlib.patches.Rectangle object at 0x7fcf5e0dcdd0>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5e0f33d0>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5e0f38d0>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5e0f3dd0>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5e101310>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5e101990>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5e101e90>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5e08f550>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5e08fbd0>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5e09b290>  …  PyObject <matplotlib.patches.Rectangle object at 0x7fcf5df34390>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5df34a10>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5df34f10>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5df41750>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5df41dd0>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5dece490>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5dece990>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5decee90>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5deda550>,PyObject <matplotlib.patches.Rectangle object at 0x7fcf5dedabd0>])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "for i = 1:TRIALS\n",
    "    sample = sum(rand(Normal(MU, SIGMA), N))\n",
    "    push!(data, sample)\n",
    "end\n",
    "\n",
    "figure()\n",
    "title(\"Sum of Normal Distributions\")\n",
    "plt[:hist](data, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f0888e548d0>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "([1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,2.0,4.0  …  6.0,3.0,3.0,6.0,5.0,0.0,0.0,0.0,1.0,2.0],[-118.575,-116.275,-113.976,-111.676,-109.376,-107.076,-104.777,-102.477,-100.177,-97.8775  …  90.6995,92.9992,95.299,97.5987,99.8984,102.198,104.498,106.798,109.097,111.397],Any[PyObject <matplotlib.patches.Rectangle object at 0x7f0884bc7910>,PyObject <matplotlib.patches.Rectangle object at 0x7f088439d4d0>,PyObject <matplotlib.patches.Rectangle object at 0x7f088439d790>,PyObject <matplotlib.patches.Rectangle object at 0x7f088439da90>,PyObject <matplotlib.patches.Rectangle object at 0x7f088439df90>,PyObject <matplotlib.patches.Rectangle object at 0x7f088432d650>,PyObject <matplotlib.patches.Rectangle object at 0x7f088432db50>,PyObject <matplotlib.patches.Rectangle object at 0x7f0884339210>,PyObject <matplotlib.patches.Rectangle object at 0x7f0884339710>,PyObject <matplotlib.patches.Rectangle object at 0x7f0884339d90>  …  PyObject <matplotlib.patches.Rectangle object at 0x7f08841d1b10>,PyObject <matplotlib.patches.Rectangle object at 0x7f08841de1d0>,PyObject <matplotlib.patches.Rectangle object at 0x7f08841de850>,PyObject <matplotlib.patches.Rectangle object at 0x7f08841deed0>,PyObject <matplotlib.patches.Rectangle object at 0x7f08841ea590>,PyObject <matplotlib.patches.Rectangle object at 0x7f08841eac10>,PyObject <matplotlib.patches.Rectangle object at 0x7f0884178150>,PyObject <matplotlib.patches.Rectangle object at 0x7f0884178650>,PyObject <matplotlib.patches.Rectangle object at 0x7f0884178b50>,PyObject <matplotlib.patches.Rectangle object at 0x7f0884184210>])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "for i = 1:TRIALS\n",
    "    sample = rand(Normal(MU, sqrt((SIGMA^2) * N)))\n",
    "    push!(data, sample)\n",
    "end\n",
    "\n",
    "figure()\n",
    "title(\"Sum of Normals\")\n",
    "plt[:hist](data, 100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.4.6",
   "language": "julia",
   "name": "julia-0.4"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.4.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}