Beta Variate Generation via Exponential Majorizing Functions

Two acceptance/rejection algorithms for generating random variates from the beta distribution are developed. The algorithms use piece-wise linear and exponential majorizing functions coupled with a piece-wise linear minorizing function. The algorithms are exact to within the accuracy of the computer...

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Published in:	Operations research Vol. 28; no. 4; pp. 917 - 926
Main Authors:	Schmeiser, Bruce W, Babu, A. J. G
Format:	Journal Article
Language:	English
Published:	Baltimore, Md INFORMS 01-07-1980 Operations Research Society of America Institute for Operations Research and the Management Sciences
Subjects:	Algorithms Beta Distribution Mathematical independent variables Mathematical models Operations research Random variables Simulation Statistics
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Abstract	Two acceptance/rejection algorithms for generating random variates from the beta distribution are developed. The algorithms use piece-wise linear and exponential majorizing functions coupled with a piece-wise linear minorizing function. The algorithms are exact to within the accuracy of the computer and are valid for all parameter values greater than one Marginal execution times are relatively insensitive to parameter values and are faster than any previously published algorithms.
AbstractList	Two acceptance/rejection algorithms for generating random variates from the beta distribution are developed. The algorithms use piece-wise linear and exponential majorizing functions coupled with a piece-wise linear minorizing function. The algorithms are exact to within the accuracy of the computer and are valid for all parameter values greater than one Marginal execution times are relatively insensitive to parameter values and are faster than any previously published algorithms. Development is traced of 2 acceptance/rejection algorithms for generating random variates from the beta distribution. These algorithms utilize piecewise linear and exponential majorizing functions which are coupled with a piecewise linear minorizing function. They are exact to within the accuracy of the computer and are valid for all parameter values that are greater than one. Marginal execution times are relatively insensitive to parameter values and are also faster than any previously published algorithms. The 2 approaches for generating pseudo-random beta variates are discussed. New algorithms are developed, and computational results concerning execution time and memory requirements are presented.
Author	Schmeiser, Bruce W Babu, A. J. G
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Copyright	Copyright 1980 The Operations Research Society of America Copyright Institute for Operations Research and the Management Sciences Jul/Aug 1980
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Snippet	Two acceptance/rejection algorithms for generating random variates from the beta distribution are developed. The algorithms use piece-wise linear and... Development is traced of 2 acceptance/rejection algorithms for generating random variates from the beta distribution. These algorithms utilize piecewise linear...
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SubjectTerms	Algorithms Beta Distribution Mathematical independent variables Mathematical models Operations research Random variables Simulation Statistics
Title	Beta Variate Generation via Exponential Majorizing Functions
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