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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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_03759601_v352_n4-5_p421_Larrondo |
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todo:paper_03759601_v352_n4-5_p421_Larrondo2023-10-03T15:31:07Z Random number generators and causality Larrondo, H.A. Martín, M.T. González, C.M. Plastino, A. Rosso, O.A. 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. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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 |
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Random number generators Statistical complexity |
| spellingShingle |
Random number generators Statistical complexity Larrondo, H.A. Martín, M.T. González, C.M. Plastino, A. Rosso, O.A. 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. |
| format |
JOUR |
| author |
Larrondo, H.A. Martín, M.T. González, C.M. Plastino, A. Rosso, O.A. |
| author_facet |
Larrondo, H.A. Martín, M.T. González, C.M. Plastino, A. Rosso, O.A. |
| author_sort |
Larrondo, H.A. |
| 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 |
| url |
http://hdl.handle.net/20.500.12110/paper_03759601_v352_n4-5_p421_Larrondo |
| work_keys_str_mv |
AT larrondoha randomnumbergeneratorsandcausality AT martinmt randomnumbergeneratorsandcausality AT gonzalezcm randomnumbergeneratorsandcausality AT plastinoa randomnumbergeneratorsandcausality AT rossooa randomnumbergeneratorsandcausality |
| _version_ |
1807318261516206080 |