Continuous random variate generation by fast numerical inversion

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...

Generation of Gaussian distributed random numbers by using a numerical inversion method

We describe a vectorizable implementation of a numerical inversion method to generate approximately Gaussian distributed random numbers. This method is, on the CRAY-YMP computer, several times faster than the standard Box—Muller—Wiener algorithm. The validity of the approximation is discussed.

Density estimation and random variate generation using multilayer networks

In this paper we consider two important topics: density estimation and random variate generation. We present a framework that is easily implemented using the familiar multilayer neural network. First, we develop two new methods for density estimation, a stochastic method and a related deterministic method. Both methods are based on approximating the distribution function, the density being obta...

Automatic Non-uniform Random Variate Generation in R

Random variate generation is an important tool in statistical computing. Many programs for simulation or statistical computing (e.g. R) provide a collection of random variate generators for many standard distributions. However, as statistical modeling has become more sophisticated there is demand for larger classes of distributions. Adding generators for newly required distributions seems not t...

An automatic code generator for nonuniform random variate generation