Intensive statistical complexity measure of pseudorandom number generators

A Statistical Complexity measure has been recently proposed to quantify the performance of chaotic Pseudorandom number generators (PRNG) (Physica A 354 (2005) 281). Here we revisit this quantifier and introduce two important improvements: (i) consideration of an intensive statistical complexity (Phy...

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Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v356_n1_p133_Larrondo
http://hdl.handle.net/20.500.12110/paper_03784371_v356_n1_p133_Larrondo
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spelling paper:paper_03784371_v356_n1_p133_Larrondo2023-06-08T15:40:03Z Intensive statistical complexity measure of pseudorandom number generators Random number generators Statistical complexity Computational complexity Evaluation Mathematical techniques Probability density function Statistical methods Intensive statistical complexity Prescription Random number generators Statistical complexity Random number generation A Statistical Complexity measure has been recently proposed to quantify the performance of chaotic Pseudorandom number generators (PRNG) (Physica A 354 (2005) 281). Here we revisit this quantifier and introduce two important improvements: (i) consideration of an intensive statistical complexity (Physica A 334 (2004) 119), and (ii) following the prescription of Brand and Pompe (Phys. Rev. Lett. 88 (2002) 174102-1) in evaluating the probability distribution associated with the PRNG. The ensuing new measure is applied to a very well-tested PRNG advanced by Marsaglia. © 2005 Elsevier B.V. All rights reserved. 2005 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v356_n1_p133_Larrondo http://hdl.handle.net/20.500.12110/paper_03784371_v356_n1_p133_Larrondo
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Random number generators
Statistical complexity
Computational complexity
Evaluation
Mathematical techniques
Probability density function
Statistical methods
Intensive statistical complexity
Prescription
Random number generators
Statistical complexity
Random number generation
spellingShingle Random number generators
Statistical complexity
Computational complexity
Evaluation
Mathematical techniques
Probability density function
Statistical methods
Intensive statistical complexity
Prescription
Random number generators
Statistical complexity
Random number generation
Intensive statistical complexity measure of pseudorandom number generators
topic_facet Random number generators
Statistical complexity
Computational complexity
Evaluation
Mathematical techniques
Probability density function
Statistical methods
Intensive statistical complexity
Prescription
Random number generators
Statistical complexity
Random number generation
description A Statistical Complexity measure has been recently proposed to quantify the performance of chaotic Pseudorandom number generators (PRNG) (Physica A 354 (2005) 281). Here we revisit this quantifier and introduce two important improvements: (i) consideration of an intensive statistical complexity (Physica A 334 (2004) 119), and (ii) following the prescription of Brand and Pompe (Phys. Rev. Lett. 88 (2002) 174102-1) in evaluating the probability distribution associated with the PRNG. The ensuing new measure is applied to a very well-tested PRNG advanced by Marsaglia. © 2005 Elsevier B.V. All rights reserved.
title Intensive statistical complexity measure of pseudorandom number generators
title_short Intensive statistical complexity measure of pseudorandom number generators
title_full Intensive statistical complexity measure of pseudorandom number generators
title_fullStr Intensive statistical complexity measure of pseudorandom number generators
title_full_unstemmed Intensive statistical complexity measure of pseudorandom number generators
title_sort intensive statistical complexity measure of pseudorandom number generators
publishDate 2005
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v356_n1_p133_Larrondo
http://hdl.handle.net/20.500.12110/paper_03784371_v356_n1_p133_Larrondo
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