Every random number generator has its advantages and deficiencies. There are no “safe” generators. The practitioner's problem is how to decide which random number generator will suit his needs best.… Expand

A random number generator produces a periodic sequence of numbers on a computer. The starting point can be random, but after it is chosen, everything else is deterministic. The goal is to “fake” the… Expand

We study the performance of AES in a series of statistical tests that are related to cryptographic notions like confusion and diffusion and provide empirical evidence for the suitability of AES for stochastic simulation.Expand

We survey the outstanding performance of inversive pseudorandom number generators in theoretical and empirical tests, in comparison to linear generators.Expand

We present a review of the main concepts to produce random numbers on parallel processors and further, we illustrate some phenomena that occur with parallelization.Expand

1. Introduction. Diaphony (see Zinterhof [13] and Kuipers and Nieder-reiter [6, Exercise 5.27, p. 162]) is a numerical quantity that measures the irregularity of the distribution of sequences in the… Expand

In this paper we discuss the theoretical analysis of correlations between pseudorandom numbers. We present a new concept that allows to relate the discrepancy approach to the spectral test. Up to… Expand

(A) generation of uniform pseudorandom numbers (in the normalized domain [0, 1[), (B) quasi-Monte Carlo methods (i.e. random samples in a Monte Carlo method are replaced by deterministic points) a… Expand