Random Variate Generation for Exponential and Gamma Tilted Stable Distributions

On random variate generation for the generalized hyperbolic secant distributions

Natural exponential families of distributions have probability mass functions of the form [exp(Ox)]#(dx) where # is a given measure, and 0 > 0 is a parameter. When we compute the mean and the variance, and force the variance to be a quadratic function of the mean as 0 is varied, the number of families becomes severely restricted. Morris (1982) showed that there are in fact only six natural expo...

Non-uniform Random Variate Generation

This is a survey of the main methods in non-uniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, and transformations are reviewed. We provide information on the expected time complexity of various algorithms, before addressing modern topics such as indirectly specified distributions, random processes, a...

Random variate generators for the Poisson-Poisson and related distributions

Random variate generators are developed for the Poisson-Poisson (or generalized Poisson) distribution. The expected time per generated random variate is uniformly bounded over the parameter space. Random variate generation for related distributions is also discussed; these include the Abel and Ressel families, and a family introduced by Haight.

Efficient matrix-exponential random variate generation using a numeric linear combination approach

Matrix Exponential (ME) probability distributions form a versatile family of distributions and enable an accurate description and simulation of a wide variety of situations where a positive distribution is required. In queueing theory they are used for arrival and service processes. Random Number Generators (RNG) for ME’s are not commonly available. In this work, we create a new ME RNG by scali...

An automatic code generator for nonuniform random variate generation