Application of finitized power series distributions to accelerated variate generation. Part II: the case of the logarithmic distribution. (12th May 2018)

• Record Type: Journal Article
• Authors: Kirtland, James Andrew; Levy, Martin S.; Cochran, James J.; Golnabi, Saeed
• Is Part Of: International transactions in operational research. Volume 27:Number 2(2020)
• Page Start: 1121
• Page End: 1132
• Publication Date: 2018-05-12
• Subjects: applied probability -- finitization -- method of aliasing -- random variate generation -- simulation; Operations research -- Periodicals; 003
• DOI: 10.1111/itor.12548
• Languages: English
• ISSNs: 0969-6016

Abstract

Abstract: The method of aliasing vastly expands the palette of discrete random variate generation methodologies while providing excellent speed. However, its application is limited to finitely supported distributions. We demonstrate that an application of moment preserving finitization called the Negative Taylor Series Finitization ( NTSF ) method for the power series family of discrete distributions, when coupled with the method of aliasing, can greatly improve infinitely supported discrete random variate generation speed with certain limitations. We illustrate this with the logarithmic power series distribution, and we compare four published algorithms designed to generate random variates from a logarithmic distribution to the aliasing method of random variate generation from an NTSF version of the same distribution. We compare the accuracy and speed (user‐time) of these various methods for generating variates from a logarithmic distribution.