On the effect of inducted negative correlation rate for beta acceptance–rejection algorithms

Moheghi, Hamidreza; Niaki, Seyed Taghi Akhavan; Bootaki, Behrang; Bakhshesh, Davood

doi:10.1080/03610918.2015.1039128

Cited by 1 publication

(1 citation statement)

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“…In this paper, ITS will be used to generate variates from a left-sided truncated Gaussian distribution by approximate the inverse mathematically. Besides the ITS method, other methods are introduced in the literature, such as the imperial method [11][12][13] and acceptance-rejection methods [14][15][16][17][18][19]. Romano [20] developed an algorithm to generate random variates from the Madland-Nix fission energy spectrum, assuming a constant compound nucleus cross-section is given based on physics considerations.…”

Section: Introductionmentioning

confidence: 99%

An approximation to the inverse of left-sided truncated gaussian cumulative normal density function using Polya's model to generate random variates for simulation applications

Hamasha¹,

Ahmed²,

Ali³

et al. 2022

J Appl Eng Science

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The Gaussian or normal distribution is vital in most areas of industrial engineering, including simulation. For example, the inverse of the Gaussian cumulative density function is used in all simulation software (e.g., ARENA, ProModel) to generate a group of random numbers that fit Gaussian distribution. It is also used to estimate the life expectancy of new devices. However, the Gaussian distribution that is truncated from the left side is not defined in any simulation software. Estimation of the expected life of used devices needs left-sided truncated Gaussian distribution. Additionally, very few works examine generating random numbers from left-sided truncated Gaussian distribution. A high accuracy mathematical-based approximation to the left-sided truncated Gaussian cumulative density function is proposed in the current work. Our approximation is built based on Polya’s approximation of the Gaussian cumulative density function. The current model is beneficial to approximate the inverse of the left-sided truncated Gaussian cumulative density function to generate random variates, which is necessary for simulation applications.

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