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Random numbers for stochastic simulation

Random numbers for stochastic simulation A method, that generates 36-bit machine-independent sets of pseudo-random numbers uniformly distributed in the interval (0.0 to 1.0), is proposed. The method has been tested on several computers, including the PDP 11/10 (16 bit), VAX 11/780 (32 bit), CII 10070 (32 bit), and UNIVAC 1110 (36 bit). The pseudo-random sequences. Normal, Log-normal, Binomial, Poisson, and Chi-squared, are calculated from the Uniform distribution. These pseudo-random numbers give perfect reproducibility regardless of the operating system and/or kind of computer used. This is of the first importance in simulation methods, like the Monte Carlo method. The cycle of the pseudo-random sequence has been tested up to 10**7. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGBIO Newsletter Association for Computing Machinery

Random numbers for stochastic simulation

ACM SIGBIO Newsletter , Volume 6 (4) – Mar 1, 1984

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Publisher
Association for Computing Machinery
Copyright
Copyright © 1984 by ACM Inc.
ISSN
0163-5697
DOI
10.1145/992237.992242
Publisher site
See Article on Publisher Site

Abstract

A method, that generates 36-bit machine-independent sets of pseudo-random numbers uniformly distributed in the interval (0.0 to 1.0), is proposed. The method has been tested on several computers, including the PDP 11/10 (16 bit), VAX 11/780 (32 bit), CII 10070 (32 bit), and UNIVAC 1110 (36 bit). The pseudo-random sequences. Normal, Log-normal, Binomial, Poisson, and Chi-squared, are calculated from the Uniform distribution. These pseudo-random numbers give perfect reproducibility regardless of the operating system and/or kind of computer used. This is of the first importance in simulation methods, like the Monte Carlo method. The cycle of the pseudo-random sequence has been tested up to 10**7.

Journal

ACM SIGBIO NewsletterAssociation for Computing Machinery

Published: Mar 1, 1984

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