Distribution of a Gaussian Random Walk

GaussianRandomWalk2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "walks = do_walk(1000,5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = np.zeros(1000)\n",
    "for i in range(1000):\n",
    "    dist[i] = walks[i][4999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1352865128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.hist(dist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "So we see that the final points of our random walks are distributed according to something that pretty much looks like a Gaussian distribution."
   ]
  }
 ],
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  "kernelspec": {
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