This is documentation for an old release of SciPy (version 0.15.1). Read this page in the documentation of the latest stable release (version 1.14.1).
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
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
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 evaulate 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.
This is also sometimes called the probability density function, although technically
is the probability density function for a discrete distribution [1] .
[1] | 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
and is also useful to be able to compute. Note that
Survival Function¶
The survival function is just
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
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
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
and
Moments¶
Non-central moments are defined using the PDF
Central moments are computed similarly \(\mu=\mu_{1}^{\prime}\)
The mean is the first moment
the variance is the second central moment
Skewness is defined as
while (Fisher) kurtosis is
so that a normal distribution has a kurtosis of zero.
Moment generating function¶
The moment generating function is defined as
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
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:
Where
Standard notation for mean¶
We will use
where \(N\) should be clear from context.
Combinations¶
Note that
and has special cases of
and
If \(n<0\) or \(k<0\) or \(k>n\) we define \(\left(\begin{array}{c} n\\ k\end{array}\right)=0\)
Discrete Distributions in scipy.stats¶
- Bernoulli Distribution
- Binomial Distribution
- Boltzmann (truncated Planck) Distribution
- Planck (discrete exponential) Distribution
- Poisson Distribution
- Geometric Distribution
- Negative Binomial Distribution
- Hypergeometric Distribution
- Zipf (Zeta) Distribution
- Logarithmic (Log-Series, Series) Distribution
- Discrete Uniform (randint) Distribution
- Discrete Laplacian Distribution