Improving randomness characterization through Bayesian model selection

Abstract Random number generation plays an essential role in technology with important applications in areas ranging from cryptography to Monte Carlo methods, and other probabilistic algorithms. All such applications require high-quality sources of random numbers, yet effective methods for assessing...

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Autores principales:	Rafael Díaz Hernández Rojas, Aldo Solís, Alí M. Angulo Martínez, Alfred B. U’Ren, Jorge G. Hirsch, Matteo Marsili, Isaac Pérez Castillo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/db59a8118f7d43a0a4495ce43f1ca6af
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spelling	oai:doaj.org-article:db59a8118f7d43a0a4495ce43f1ca6af2021-12-02T12:30:45ZImproving randomness characterization through Bayesian model selection10.1038/s41598-017-03185-y2045-2322https://doaj.org/article/db59a8118f7d43a0a4495ce43f1ca6af2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03185-yhttps://doaj.org/toc/2045-2322Abstract Random number generation plays an essential role in technology with important applications in areas ranging from cryptography to Monte Carlo methods, and other probabilistic algorithms. All such applications require high-quality sources of random numbers, yet effective methods for assessing whether a source produce truly random sequences are still missing. Current methods either do not rely on a formal description of randomness (NIST test suite) on the one hand, or are inapplicable in principle (the characterization derived from the Algorithmic Theory of Information), on the other, for they require testing all the possible computer programs that could produce the sequence to be analysed. Here we present a rigorous method that overcomes these problems based on Bayesian model selection. We derive analytic expressions for a model’s likelihood which is then used to compute its posterior distribution. Our method proves to be more rigorous than NIST’s suite and Borel-Normality criterion and its implementation is straightforward. We applied our method to an experimental device based on the process of spontaneous parametric downconversion to confirm it behaves as a genuine quantum random number generator. As our approach relies on Bayesian inference our scheme transcends individual sequence analysis, leading to a characterization of the source itself.Rafael Díaz Hernández RojasAldo SolísAlí M. Angulo MartínezAlfred B. U’RenJorge G. HirschMatteo MarsiliIsaac Pérez CastilloNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-6 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rafael Díaz Hernández Rojas Aldo Solís Alí M. Angulo Martínez Alfred B. U’Ren Jorge G. Hirsch Matteo Marsili Isaac Pérez Castillo Improving randomness characterization through Bayesian model selection
description	Abstract Random number generation plays an essential role in technology with important applications in areas ranging from cryptography to Monte Carlo methods, and other probabilistic algorithms. All such applications require high-quality sources of random numbers, yet effective methods for assessing whether a source produce truly random sequences are still missing. Current methods either do not rely on a formal description of randomness (NIST test suite) on the one hand, or are inapplicable in principle (the characterization derived from the Algorithmic Theory of Information), on the other, for they require testing all the possible computer programs that could produce the sequence to be analysed. Here we present a rigorous method that overcomes these problems based on Bayesian model selection. We derive analytic expressions for a model’s likelihood which is then used to compute its posterior distribution. Our method proves to be more rigorous than NIST’s suite and Borel-Normality criterion and its implementation is straightforward. We applied our method to an experimental device based on the process of spontaneous parametric downconversion to confirm it behaves as a genuine quantum random number generator. As our approach relies on Bayesian inference our scheme transcends individual sequence analysis, leading to a characterization of the source itself.
format	article
author	Rafael Díaz Hernández Rojas Aldo Solís Alí M. Angulo Martínez Alfred B. U’Ren Jorge G. Hirsch Matteo Marsili Isaac Pérez Castillo
author_facet	Rafael Díaz Hernández Rojas Aldo Solís Alí M. Angulo Martínez Alfred B. U’Ren Jorge G. Hirsch Matteo Marsili Isaac Pérez Castillo
author_sort	Rafael Díaz Hernández Rojas
title	Improving randomness characterization through Bayesian model selection
title_short	Improving randomness characterization through Bayesian model selection
title_full	Improving randomness characterization through Bayesian model selection
title_fullStr	Improving randomness characterization through Bayesian model selection
title_full_unstemmed	Improving randomness characterization through Bayesian model selection
title_sort	improving randomness characterization through bayesian model selection
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/db59a8118f7d43a0a4495ce43f1ca6af
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Improving randomness characterization through Bayesian model selection

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