Non-uniform random variate generation

Generating pseudo-random numbers that follow a probability distribution / From Wikipedia, the free encyclopedia

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,[citation needed] published by John von Neumann in the early 1950s.[1]