# Discrete Statistical Distributions¶

Discrete random variables take on only a countable number of values. The commonly used distributions are included in SciPy and described in this document. Each discrete distribution can take one extra integer parameter: $$L.$$ The relationship between the general distribution $$p$$ and the standard distribution $$p_{0}$$ is

p\left(x\right) = p_{0}\left(x-L\right)

which allows for shifting of the input. When a distribution generator is initialized, the discrete distribution can either specify the beginning and ending (integer) values $$a$$ and $$b$$ which must be such that

p_{0}\left(x\right) = 0\quad x < a \textrm{ or } x > b

in which case, it is assumed that the pdf function is specified on the integers $$a+mk\leq b$$ where $$k$$ is a non-negative integer ( $$0,1,2,\ldots$$ ) and $$m$$ is a positive integer multiplier. Alternatively, the two lists $$x_{k}$$ and $$p\left(x_{k}\right)$$ can be provided directly in which case a dictionary is set up internally to evaluate probabilities and generate random variates.

## Probability Mass Function (PMF)¶

The probability mass function of a random variable X is defined as the probability that the random variable takes on a particular value.

p\left(x_{k}\right)=P\left[X=x_{k}\right]

This is also sometimes called the probability density function, although technically

f\left(x\right)=\sum_{k}p\left(x_{k}\right)\delta\left(x-x_{k}\right)

is the probability density function for a discrete distribution  .

  XXX: Unknown layout Plain Layout: Note that we will be using $$p$$ to represent the probability mass function and a parameter (a XXX: probability). The usage should be obvious from context.

## Cumulative Distribution Function (CDF)¶

The cumulative distribution function is

F\left(x\right)=P\left[X\leq x\right]=\sum_{x_{k}\leq x}p\left(x_{k}\right)

and is also useful to be able to compute. Note that

F\left(x_{k}\right)-F\left(x_{k-1}\right)=p\left(x_{k}\right)

## Survival Function¶

The survival function is just

S\left(x\right)=1-F\left(x\right)=P\left[X>k\right]

the probability that the random variable is strictly larger than $$k$$ .

## Percent Point Function (Inverse CDF)¶

The percent point function is the inverse of the cumulative distribution function and is

G\left(q\right)=F^{-1}\left(q\right)

for discrete distributions, this must be modified for cases where there is no $$x_{k}$$ such that $$F\left(x_{k}\right)=q.$$ In these cases we choose $$G\left(q\right)$$ to be the smallest value $$x_{k}=G\left(q\right)$$ for which $$F\left(x_{k}\right)\geq q$$ . If $$q=0$$ then we define $$G\left(0\right)=a-1$$ . This definition allows random variates to be defined in the same way as with continuous rv’s using the inverse cdf on a uniform distribution to generate random variates.

## Inverse survival function¶

The inverse survival function is the inverse of the survival function

Z\left(\alpha\right)=S^{-1}\left(\alpha\right)=G\left(1-\alpha\right)

and is thus the smallest non-negative integer $$k$$ for which $$F\left(k\right)\geq1-\alpha$$ or the smallest non-negative integer $$k$$ for which $$S\left(k\right)\leq\alpha.$$

## Hazard functions¶

If desired, the hazard function and the cumulative hazard function could be defined as

h\left(x_{k}\right)=\frac{p\left(x_{k}\right)}{1-F\left(x_{k}\right)}

and

H\left(x\right)=\sum_{x_{k}\leq x}h\left(x_{k}\right)=\sum_{x_{k}\leq x}\frac{F\left(x_{k}\right)-F\left(x_{k-1}\right)}{1-F\left(x_{k}\right)}.

## Moments¶

Non-central moments are defined using the PDF

\mu_{m}^{\prime}=E\left[X^{m}\right]=\sum_{k}x_{k}^{m}p\left(x_{k}\right).

Central moments are computed similarly $$\mu=\mu_{1}^{\prime}$$

\begin{eqnarray*} \mu_{m}=E\left[\left(X-\mu\right)^{m}\right] & = & \sum_{k}\left(x_{k}-\mu\right)^{m}p\left(x_{k}\right)\\ & = & \sum_{k=0}^{m}\left(-1\right)^{m-k}\left(\begin{array}{c} m\\ k\end{array}\right)\mu^{m-k}\mu_{k}^{\prime}\end{eqnarray*}

The mean is the first moment

\mu=\mu_{1}^{\prime}=E\left[X\right]=\sum_{k}x_{k}p\left(x_{k}\right)

the variance is the second central moment

\mu_{2}=E\left[\left(X-\mu\right)^{2}\right]=\sum_{x_{k}}x_{k}^{2}p\left(x_{k}\right)-\mu^{2}.

Skewness is defined as

\gamma_{1}=\frac{\mu_{3}}{\mu_{2}^{3/2}}

while (Fisher) kurtosis is

\gamma_{2}=\frac{\mu_{4}}{\mu_{2}^{2}}-3,

so that a normal distribution has a kurtosis of zero.

## Moment generating function¶

The moment generating function is defined as

M_{X}\left(t\right)=E\left[e^{Xt}\right]=\sum_{x_{k}}e^{x_{k}t}p\left(x_{k}\right)

Moments are found as the derivatives of the moment generating function evaluated at $$0.$$

## Fitting data¶

To fit data to a distribution, maximizing the likelihood function is common. Alternatively, some distributions have well-known minimum variance unbiased estimators. These will be chosen by default, but the likelihood function will always be available for minimizing.

If $$f_{i}\left(k;\boldsymbol{\theta}\right)$$ is the PDF of a random-variable where $$\boldsymbol{\theta}$$ is a vector of parameters ( e.g. $$L$$ and $$S$$ ), then for a collection of $$N$$ independent samples from this distribution, the joint distribution the random vector $$\mathbf{k}$$ is

f\left(\mathbf{k};\boldsymbol{\theta}\right)=\prod_{i=1}^{N}f_{i}\left(k_{i};\boldsymbol{\theta}\right).

The maximum likelihood estimate of the parameters $$\boldsymbol{\theta}$$ are the parameters which maximize this function with $$\mathbf{x}$$ fixed and given by the data:

\begin{eqnarray*} \hat{\boldsymbol{\theta}} & = & \arg\max_{\boldsymbol{\theta}}f\left(\mathbf{k};\boldsymbol{\theta}\right)\\ & = & \arg\min_{\boldsymbol{\theta}}l_{\mathbf{k}}\left(\boldsymbol{\theta}\right).\end{eqnarray*}

Where

\begin{eqnarray*} l_{\mathbf{k}}\left(\boldsymbol{\theta}\right) & = & -\sum_{i=1}^{N}\log f\left(k_{i};\boldsymbol{\theta}\right)\\ & = & -N\overline{\log f\left(k_{i};\boldsymbol{\theta}\right)}\end{eqnarray*}

## Standard notation for mean¶

We will use

\overline{y\left(\mathbf{x}\right)}=\frac{1}{N}\sum_{i=1}^{N}y\left(x_{i}\right)

where $$N$$ should be clear from context.

## Combinations¶

Note that

k!=k\cdot\left(k-1\right)\cdot\left(k-2\right)\cdot\cdots\cdot1=\Gamma\left(k+1\right)

and has special cases of

\begin{eqnarray*} 0! & \equiv & 1\\ k! & \equiv & 0\quad k<0\end{eqnarray*}

and

\left(\begin{array}{c} n\\ k\end{array}\right)=\frac{n!}{\left(n-k\right)!k!}.

If $$n<0$$ or $$k<0$$ or $$k>n$$ we define $$\left(\begin{array}{c} n\\ k\end{array}\right)=0$$