Gaussian random number generators
- 2 November 2007
- journal article
- Published by Association for Computing Machinery (ACM) in ACM Computing Surveys
- Vol. 39 (4)
- https://doi.org/10.1145/1287620.1287622
Abstract
Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent requirements on the quality of Gaussian random number generators (GRNG). This article describes the algorithms underlying various GRNGs, compares their computational requirements, and examines the quality of the random numbers with emphasis on the behaviour in the tail region of the Gaussian probability density function.Keywords
This publication has 36 references indexed in Scilit:
- The Monty Python method for generating random variablesACM Transactions on Mathematical Software, 1998
- Fast pseudorandom generators for normal and exponential variatesACM Transactions on Mathematical Software, 1996
- Maximally equidistributed combined Tausworthe generatorsMathematics of Computation, 1996
- A note on the quality of random variates generated by the ratio of uniforms methodACM Transactions on Modeling and Computer Simulation, 1994
- A fast normal random number generatorACM Transactions on Mathematical Software, 1992
- An Efficient Method for Generating Discrete Random Variables with General DistributionsACM Transactions on Mathematical Software, 1977
- Algorithm 488: A Gaussian pseudo-random number generatorCommunications of the ACM, 1974
- Evaluation of a Pseudorandom Normal Number GeneratorJournal of the ACM, 1964
- Generating a Variable from the Tail of the Normal DistributionTechnometrics, 1964
- A Comparison of Methods for Generating Normal Deviates on Digital ComputersJournal of the ACM, 1959