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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

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Titel: 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
Autoren: Hamasha Mohammad M., Ahmed Abdulaziz, Ali Haneen, Hamasha Sa'd, Aqlan Faisal
Quelle: Istrazivanja i projektovanja za privredu, Vol 20, Iss 2, Pp 582-589 (2022)
Verlagsinformationen: Institut za istrazivanja i projektovanja u privredi
Publikationsjahr: 2022
Bestand: Directory of Open Access Journals: DOAJ Articles
Schlagwörter: gaussian distribution, normal distribution, random variate generation, cumulative density function, mathematical approximation, truncated normal distribution, Technology, Engineering (General). Civil engineering (General), TA1-2040
Beschreibung: 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.
Publikationsart: article in journal/newspaper
Sprache: English
Relation: https://scindeks-clanci.ceon.rs/data/pdf/1451-4117/2022/1451-41172202582H.pdf; https://doaj.org/toc/1451-4117; https://doaj.org/toc/1821-3197; https://doaj.org/article/ac1b2ca2402b4f258c7d324d4642ffd3
DOI: 10.5937/jaes0-35413
Verfügbarkeit: https://doi.org/10.5937/jaes0-35413
https://doaj.org/article/ac1b2ca2402b4f258c7d324d4642ffd3
Dokumentencode: edsbas.FC2E0268
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  Data: 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
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  Data: Istrazivanja i projektovanja za privredu, Vol 20, Iss 2, Pp 582-589 (2022)
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  Data: Institut za istrazivanja i projektovanja u privredi
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  Data: 2022
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  Label: Description
  Group: Ab
  Data: 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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  Data: https://scindeks-clanci.ceon.rs/data/pdf/1451-4117/2022/1451-41172202582H.pdf; https://doaj.org/toc/1451-4117; https://doaj.org/toc/1821-3197; https://doaj.org/article/ac1b2ca2402b4f258c7d324d4642ffd3
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        Value: 10.5937/jaes0-35413
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: gaussian distribution
        Type: general
      – SubjectFull: normal distribution
        Type: general
      – SubjectFull: random variate generation
        Type: general
      – SubjectFull: cumulative density function
        Type: general
      – SubjectFull: mathematical approximation
        Type: general
      – SubjectFull: truncated normal distribution
        Type: general
      – SubjectFull: Technology
        Type: general
      – SubjectFull: Engineering (General). Civil engineering (General)
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