A wide range of Gaussian random number generators (GRNGs) have been described in the literature. 39, 4, Article 11 (October 2007), 38 pages DOI = / INTRODUCTION Simulations requiring Gaussian random numbers are critical in fields including communications, financial modelling, and many others. Categories and Subject Descriptors: G.3 : Random number generation General Terms: Algorithms, Performance Additional Key Words and Phrases: Random numbers, Gaussian, normal, simulation ACM Reference Format: Thomas, D. 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.
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).
VILLASENOR University of California, Los Angeles Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. LEONG The Chinese University of Hong Kong and Imperial College and JOHN D. THOMAS and WAYNE LUK Imperial College 11 PHILIP H.W.
1 Gaussian Random Number Generators DAVID B.
0 kommentar(er)
