Random number generators and causality

We advance a prescription to randomize physical or algorithmic Random Number Generators (RNG's) that do not pass Marsaglia's DIEHARD test suite and discuss a special physical quantifier, based on an intensive statistical complexity measure, that is able to adequately assess the improvement...

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Detalles Bibliográficos
Publicado: 2006
Materias:
Random number generators
Statistical complexity
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03759601_v352_n4-5_p421_Larrondo
http://hdl.handle.net/20.500.12110/paper_03759601_v352_n4-5_p421_Larrondo
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_03759601_v352_n4-5_p421_Larrondo
record_format dspace
spelling paper:paper_03759601_v352_n4-5_p421_Larrondo2023-06-08T15:38:39Z Random number generators and causality Random number generators Statistical complexity We advance a prescription to randomize physical or algorithmic Random Number Generators (RNG's) that do not pass Marsaglia's DIEHARD test suite and discuss a special physical quantifier, based on an intensive statistical complexity measure, that is able to adequately assess the improvements produced thereby. Eight RNG's are evaluated and the associated results are compared to those obtained by recourse to Marsaglia's DIEHARD test suite. Our quantifier, which is evaluated using causality arguments, can forecast whether a given RNG will pass the above mentioned test. © 2005 Elsevier B.V. All rights reserved. 2006 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03759601_v352_n4-5_p421_Larrondo http://hdl.handle.net/20.500.12110/paper_03759601_v352_n4-5_p421_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
spellingShingle Random number generators
Statistical complexity
Random number generators and causality
topic_facet Random number generators
Statistical complexity
description We advance a prescription to randomize physical or algorithmic Random Number Generators (RNG's) that do not pass Marsaglia's DIEHARD test suite and discuss a special physical quantifier, based on an intensive statistical complexity measure, that is able to adequately assess the improvements produced thereby. Eight RNG's are evaluated and the associated results are compared to those obtained by recourse to Marsaglia's DIEHARD test suite. Our quantifier, which is evaluated using causality arguments, can forecast whether a given RNG will pass the above mentioned test. © 2005 Elsevier B.V. All rights reserved.
title Random number generators and causality
title_short Random number generators and causality
title_full Random number generators and causality
title_fullStr Random number generators and causality
title_full_unstemmed Random number generators and causality
title_sort random number generators and causality
publishDate 2006
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03759601_v352_n4-5_p421_Larrondo
http://hdl.handle.net/20.500.12110/paper_03759601_v352_n4-5_p421_Larrondo
_version_ 1768546494718148608

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