Chi-square of Pseudorandom Number Generator of Normal Distribution in C++17

High quality pseudorandom number generators were needed in many software solutions throughout the history of programming. Nowadays, these generators play an even more significant role in software development. Generally, these generators bring a certain level of coincidence in some algorithms which n...

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Autores principales: Pavel Tomášek, Hana Tomášková, Jakub Rak
Formato: article
Lenguaje:EN
Publicado: UIKTEN 2021
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Acceso en línea:https://doaj.org/article/0fe464c66c2d4995addb4653fcd3ee99
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spelling oai:doaj.org-article:0fe464c66c2d4995addb4653fcd3ee992021-11-30T20:08:40ZChi-square of Pseudorandom Number Generator of Normal Distribution in C++1710.18421/TEM104-012217-83092217-8333https://doaj.org/article/0fe464c66c2d4995addb4653fcd3ee992021-11-01T00:00:00Zhttps://www.temjournal.com/content/104/TEMJournalNovember2021_1495_1499.pdfhttps://doaj.org/toc/2217-8309https://doaj.org/toc/2217-8333High quality pseudorandom number generators were needed in many software solutions throughout the history of programming. Nowadays, these generators play an even more significant role in software development. Generally, these generators bring a certain level of coincidence in some algorithms which need it. This work focuses on the statistical evaluation of one of the representatives of the generators using Pearson's Chi-square goodness of fit test. The generator of pseudorandom numbers under test is the specific implementation in the modern standard of the programming language of C++ (the standard of C++17). Results presented in this paper inform whether the numbers generated by the selected generator follow the desired probability distribution (normal).Pavel TomášekHana TomáškováJakub RakUIKTENarticlechi-squarepseudorandom number generatorc++normal distributionEducationLTechnologyTENTEM Journal, Vol 10, Iss 4, Pp 1495-1499 (2021)
institution DOAJ
collection DOAJ
language EN
topic chi-square
pseudorandom number generator
c++
normal distribution
Education
L
Technology
T
spellingShingle chi-square
pseudorandom number generator
c++
normal distribution
Education
L
Technology
T
Pavel Tomášek
Hana Tomášková
Jakub Rak
Chi-square of Pseudorandom Number Generator of Normal Distribution in C++17
description High quality pseudorandom number generators were needed in many software solutions throughout the history of programming. Nowadays, these generators play an even more significant role in software development. Generally, these generators bring a certain level of coincidence in some algorithms which need it. This work focuses on the statistical evaluation of one of the representatives of the generators using Pearson's Chi-square goodness of fit test. The generator of pseudorandom numbers under test is the specific implementation in the modern standard of the programming language of C++ (the standard of C++17). Results presented in this paper inform whether the numbers generated by the selected generator follow the desired probability distribution (normal).
format article
author Pavel Tomášek
Hana Tomášková
Jakub Rak
author_facet Pavel Tomášek
Hana Tomášková
Jakub Rak
author_sort Pavel Tomášek
title Chi-square of Pseudorandom Number Generator of Normal Distribution in C++17
title_short Chi-square of Pseudorandom Number Generator of Normal Distribution in C++17
title_full Chi-square of Pseudorandom Number Generator of Normal Distribution in C++17
title_fullStr Chi-square of Pseudorandom Number Generator of Normal Distribution in C++17
title_full_unstemmed Chi-square of Pseudorandom Number Generator of Normal Distribution in C++17
title_sort chi-square of pseudorandom number generator of normal distribution in c++17
publisher UIKTEN
publishDate 2021
url https://doaj.org/article/0fe464c66c2d4995addb4653fcd3ee99
work_keys_str_mv AT paveltomasek chisquareofpseudorandomnumbergeneratorofnormaldistributioninc17
AT hanatomaskova chisquareofpseudorandomnumbergeneratorofnormaldistributioninc17
AT jakubrak chisquareofpseudorandomnumbergeneratorofnormaldistributioninc17
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