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    "# Simulating Random Variables with Inverse Transform Sampling\n",
    "\n",
    "In studying the transformation of random variables in [All of Statistics](http://www.stat.cmu.edu/~larry/all-of-statistics/) and working on a [few](/ml/hw/all-of-statistics-ch2-cdf-proof/) [related](/ml/hw/all-of-statistics-ch2-problem-04/) [exercises](/ml/hw/all-of-statistics-ch2-problem-08/) I've been in search of bigger picture motivation and a very cool result finally clicked: that we can simulate samples from any distribution by applying its inverse CDF to samples taken from a uniform random variable. (Note: technically this only works when the CDF has a closed form inverse function). Or in plainer terms, how can we transform results from `math.random()` to simulate draws from another distribution?\n",
    "\n",
    "## Transformation of Random Variables\n",
    "\n",
    "Let's consider how to take the transformation of a random variable $X$ with cumulative distribution function $F_X(x)$. Let $Y = r(X)$, that is, $Y$ is the transformation of $X$ via function $r(X)$. \n",
    "\n",
    "To get the CDF of $Y$ we use the definition of CDFs:\n",
    "\n",
    "$F_Y(y) = P(Y \\leq y) = P(r(X) \\leq y)$\n",
    "\n",
    "We have $F_X(x)$ and want to know how to compute $F_Y(y)$ in terms of $F_X(x)$. To get there we can take the inverse of $r(x)$ on both sides of the inequality:\n",
    "\n",
    "$F_Y(y) = P(Y \\leq y) = P(r(X) \\leq y) = P(X \\leq r^{-1}(y))$\n",
    "\n",
    "hey, that kind of looks like the CDF of $X$: \n",
    "\n",
    "$P(X \\leq r^{-1}(y)) = F_X(r^{-1}(y))$\n",
    "\n",
    "and that's how we get $F_Y(y)$ in terms of $F_X(x)$. We can compute the density function $f_y(y)$ by differentiating $F_Y(y)$, applying the chain rule:\n",
    "\n",
    "$f_y(y) = f_y(r^{-1}(y)) \\times \\frac{d}{dy} r^{-1}(y)\\mathop{dy}$\n",
    "\n",
    "Note that it is only this simple if $r(x)$ is one-to-one and strictly monotone increasing; it gets more complicated to reason about the regions where $Y$ is defined otherwise, but this hopefully provides the background for why we use with the inverse of the CDF.\n",
    "\n",
    "\n",
    "## Transforming a Uniform Random Variable\n",
    "\n",
    "Getting back to the \"cool result\" I mentioned earlier, let's put this to use to show how we can take samples from other distributions if all we have available is a random number generator. [Random number generation](http://www.eg.bucknell.edu/~xmeng/Course/CS6337/Note/master/node36.html) is a whole other topic, but you are likely familiar with a `random` function available in most languages that will give you a number between 0 and 1. This can be viewed as a function that will give you samples from a uniform random variable in the range 0 to 1.\n",
    "\n",
    "So we have a uniform random variable $U$ and another distribution $X$ with CDF $F$ that we'd like to draw samples from. If we'd like to be able to get $X$ as a function of $F$ and $U$, we need to work backwards and use the *inverse* of $F$ as the transformation function of $U$, so that when we apply the inverse of the inverse we get back to $X$:\n",
    "\n",
    "$F_X(x) = P(F^{-1}(U) \\leq x) = P(U \\leq F(x)) = F_U(F_X(x))$ \n",
    "\n",
    "$F_U(F(x)) =\n",
    "\\begin{cases}\n",
    "0 & F(x) < 0 \\\\\\\n",
    "F(x) & 0 \\leq F(x) \\leq 1 \\\\\\\n",
    "1 & F(x) > 1\n",
    "\\end{cases}\n",
    "$\n",
    "\n",
    "Note that $F(x)$, being a CDF, can only take ranges from 0 to 1, so this collapses to just:\n",
    "\n",
    "$F_X(x) = F_U(F(x)) = F(x)$ wherever $F(x)$ is defined, so $X \\sim F$.\n",
    "\n",
    "Now that we've established that $X = F^{-1}(U)$, let's put it to use in code.\n",
    "\n",
    "## Sampling from Uniform\n",
    "\n",
    "First, let's plot a histogram of samples from the $U(0, 1)$:\n"
   ]
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   "execution_count": 1,
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   "outputs": [
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7633VXl+bfVnf9PRVe31t9lV7fW32ZX3T01ft9bXZV+31tdmXoX3wg8so+2X0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x101fbaeb8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 374
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist([random.random() for i in range(10000)], 50, normed=True)\n",
    "plt.title('Samples from Uniform(0,1)')\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "yep, that looks pretty uniform. \n",
    "\n",
    "## Sampling from an Exponential Distribution\n",
    "\n",
    "Now let's say we want to sample from an [exponential distribution](https://en.wikipedia.org/wiki/Exponential_distribution) to simulate the interval between requests hitting a web server (or see [plenty of other examples](https://www.probabilitycourse.com/chapter11/11_1_2_basic_concepts_of_the_poisson_process.php) of where this function might come in handy).\n",
    "\n",
    "The exponential distribution has CDF:\n",
    "\n",
    "$F_X(x) = 1 - e^{-\\lambda x}$\n",
    "\n",
    "following the plan outlined above, we can create our own exponential sampling function based on the random number generator if we can compute the CDF's inverse:\n",
    "\n",
    "$1 - e^{-\\lambda x} = y$\n",
    "<br>$e^{-\\lambda x} = 1 - y$\n",
    "<br>$-\\lambda x = ln(1 - y)$\n",
    "<br>$x = \\frac{-1}{\\lambda}ln(1 - y)$\n",
    "\n",
    "so \n",
    "\n",
    "$F^{-1}_X(x) = \\frac{-1}{\\lambda}ln(1 - x)$\n",
    "\n",
    "Applying this inverse function to a sample taken from our random number generator should do the trick. Let's write this function and plot its histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LkiRJDRmQS5IkSQ0ZkEuSJEkNLcuAPCJm9Tr66KNbF1mSJElbqGUZkEuSJElL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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10809c400>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 370
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def my_exp(lmbda=1.0):\n",
    "    return (-1 / lmbda)*math.log(1 - random.random())\n",
    "\n",
    "plt.hist([my_exp() for i in range(10000)], 50, normed=True)\n",
    "plt.title('Samples from hand rolled exponential function')\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and just to make sure this looks right, let's use `numpy`'s exponential function and compare:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VCesqSZIkTZ1JA/ludXrRkOUX1+muowrJzLXAQZSW8PMj4iMR8a6I+CxwGiXw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x101fbac50>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 370
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy.random\n",
    "\n",
    "plt.hist([numpy.random.exponential() for i in range(10000)], 50, normed=True)   \n",
    "plt.title('Samples from numpy.random.exponential')\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking good.\n",
    "\n",
    "## Why does this work?\n",
    "\n",
    "To get some intuition as to why this works, picture a cumulative distribution function and imagine taking a random point on the Y axis and seeing where it intersects the CDF.\n",
    "\n",
    "Here's a plot of where 0.7 intersects the CDF for the Exponential distribution:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3bx6ceSb8/vfp/mabwXXXpakNJUlSy5XEJGQhhC7AJOAC4BpgPaAnsBUwDbgl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\nIi6KiCnn6X5vWVnRjI9Osn8b5Qz6cuCerlQkqa9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1087b2080>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 370
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import expon\n",
    "\n",
    "x = np.arange(0, 5, 0.005)\n",
    "plt.plot(x, expon.cdf(x))\n",
    "plt.plot(x, [0.7 for el in x], 'r--')\n",
    "plt.title('CDF of Exponential')\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this is exactly what it means to take the inverse; where the dotted line intersects is $F^{-1}(0.7)$. \n",
    "\n",
    "So with a random number generator in hand, we can randomly start on the Y axis and use a random variable's inverse CDF function to get back to a point sampled according to its distribution.\n",
    "\n",
    "Further reading:\n",
    "- [Notes on transformation of random variables](http://math.arizona.edu/~jwatkins/f-transform.pdf)\n",
    "- [Notes on generating RVs](http://www.eg.bucknell.edu/~xmeng/Course/CS6337/Note/master/node48.html)\n",
    "- [Excellent Quora answer providing further intuition](https://www.quora.com/What-is-an-intuitive-explanation-of-inverse-transform-sampling-method-in-statistics-and-how-does-it-relate-to-cumulative-distribution-function/answer/Amit-Sharma-2?srid=X8V)"
   ]
  }
 ],
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