Non-uniform random variate generation
# Non-uniform random variate generation

Non-uniform random variate generation or pseudo-random number sampling is the numerical practice of generating pseudo-random numbers (PRN) that follow a given probability distribution. Methods are typically based on the availability of a uniformly distributed PRN generator. Computational algorithms are then used to manipulate a single random variate, X, or often several such variates, into a new random variate Y such that these values have the required distribution. The first methods were developed for Monte-Carlo simulations in the Manhattan project, published by John von Neumann in the early 1950s.

## Finite discrete distributions

For a discrete probability distribution with a finite number n of indices at which the probability mass function f takes non-zero values, the basic sampling algorithm is straightforward. The interval [0, 1) is divided in n intervals [0, f(1)), [f(1), f(1) + f(2)), ... The width of interval i equals the probability f(i). One draws a uniformly distributed pseudo-random number X, and searches for the index i of the corresponding interval. The so determined i will have the distribution f(i).

Formalizing this idea becomes easier by using the cumulative distribution function

    $F(i)=\sum _{j=1}^{i}f(j).$

It is convenient to set F(0) = 0. The n intervals are then simply [F(0), F(1)), [F(1), F(2)), ..., [F(n − 1), F(n)). The main computational task is then to determine i for which F(i − 1) ≤ X < F(i).

This can be done by different algorithms:

* Linear search, computational time linear in n.
* Binary search, computational time goes with log n.
* Indexed search, also called the cutpoint method.
* Alias method, computational time is constant, using some pre-computed tables.
* There are other methods that cost constant time.

## Continuous distributions

Generic methods for generating independent samples:

* Rejection sampling for arbitrary density functions
* Inverse transform sampling for distributions whose CDF is known
* Ratio of uniforms, combining a change of variables and rejection sampling
* Slice sampling
* Ziggurat algorithm, for monotonically decreasing density functions as well as symmetric unimodal distributions
* Convolution random number generator, not a sampling method in itself: it describes the use of arithmetics on top of one or more existing sampling methods to generate more involved distributions.

Generic methods for generating correlated samples (often necessary for unusually-shaped or high-dimensional distributions):

* Markov chain Monte Carlo, the general principle
* Metropolis–Hastings algorithm
* Gibbs sampling
* Slice sampling
* Reversible-jump Markov chain Monte Carlo, when the number of dimensions is not fixed (e.g. when estimating a mixture model and simultaneously estimating the number of mixture components)
* Particle filters, when the observed data is connected in a Markov chain and should be processed sequentially

For generating a normal distribution:

* Box–Muller transform
* Marsaglia polar method

For generating a Poisson distribution:

* See Poisson distribution#Generating Poisson-distributed random variables

## Software libraries

GNU Scientific Library has a section entitled "Random Number Distributions" with routines for sampling under more than twenty different distributions.

