Performance comparison of random number generators based on Adaptive Rejection Sampling

Random-number Generators Physical Random-number Generators

R andom numbers have applications in many areas: simulation, game-playing, cryptography, statistical sampling, evaluation of multiple integrals, particletransport calculations, and computations in statistical physics, to name a few. Since each application involves slightly different criteria for judging the “worthiness” of the random numbers generated, a variety of generators have been develope...

Performance and Quality of Random Number Generators

Random number generation continues to be a critical component in much of computational science and the tradeoff between quality and computational performance is a key issue for many numerical simulations. We review the performance and statistical quality of some well known algorithms for generating pseudo random numbers. Graphical Processing Units (GPUs) are a powerful platform for accelerating...

On Uniform Random Number Generators

Adaptive Rejection Sampling with fixed number of nodes

The adaptive rejection sampling (ARS) algorithm is a universal random generator for drawing samples efficiently from a univariate log-concave target probability density function (pdf). ARS generates independent samples from the target via rejection sampling with high acceptance rates. Indeed, ARS yields a sequence of proposal functions that converge toward the target pdf, so that the probabilit...

Chaos-based true random number generators

*Correspondence: [email protected] Gregorio Millán Institute for Fluid Dynamics, Nanoscience and Industrial Mathematics, Universidad Carlos III de Madrid, Avenida de la Universidad 30, Leganés, 28911, Spain Abstract Random number (bit) generators are crucial to secure communications, data transfer and storage, and electronic transactions, to carry out stochastic simulations and to many other ...