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Certain Relations in Statistical Physics Based on Rényi Entropy

The statistical theory based on the parametric family of Rényi entropy functionals is a generalization of Gibbs statistics. Depending on the value of the involved parameter, the corresponding Rényi distribution can take both an exponential form and a power-law form, which is typical for a wide range...

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Detalles Bibliográficos
Autores principales: Bakiev, T. N., Nakashidze, D. V., Savchenko, A. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Pleiades Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955908/
http://dx.doi.org/10.3103/S002713492006003X
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author Bakiev, T. N.
Nakashidze, D. V.
Savchenko, A. M.
author_facet Bakiev, T. N.
Nakashidze, D. V.
Savchenko, A. M.
author_sort Bakiev, T. N.
collection PubMed
description The statistical theory based on the parametric family of Rényi entropy functionals is a generalization of Gibbs statistics. Depending on the value of the involved parameter, the corresponding Rényi distribution can take both an exponential form and a power-law form, which is typical for a wide range of statistical models. In this paper, we prove the energy equipartition theorem in the case of Rényi statistics, which makes it possible to solve the problem of obtaining the average energy for a large number of classical statistical models. The proposed approach for calculating the average energy is compared with the procedure for directly calculating this quantity for a system described by the simplest power-low Hamiltonian. New relations are presented that simplify the calculations in the considered theory. A special case of the Rényi distribution, which represents a generalization of a power-low distribution and thus allows us to approximate some empirical data more precisely, has been studied.
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spelling pubmed-79559082021-03-15 Certain Relations in Statistical Physics Based on Rényi Entropy Bakiev, T. N. Nakashidze, D. V. Savchenko, A. M. Moscow Univ. Phys. Article The statistical theory based on the parametric family of Rényi entropy functionals is a generalization of Gibbs statistics. Depending on the value of the involved parameter, the corresponding Rényi distribution can take both an exponential form and a power-law form, which is typical for a wide range of statistical models. In this paper, we prove the energy equipartition theorem in the case of Rényi statistics, which makes it possible to solve the problem of obtaining the average energy for a large number of classical statistical models. The proposed approach for calculating the average energy is compared with the procedure for directly calculating this quantity for a system described by the simplest power-low Hamiltonian. New relations are presented that simplify the calculations in the considered theory. A special case of the Rényi distribution, which represents a generalization of a power-low distribution and thus allows us to approximate some empirical data more precisely, has been studied. Pleiades Publishing 2021-03-14 2020 /pmc/articles/PMC7955908/ http://dx.doi.org/10.3103/S002713492006003X Text en © Allerton Press, Inc. 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Bakiev, T. N.
Nakashidze, D. V.
Savchenko, A. M.
Certain Relations in Statistical Physics Based on Rényi Entropy
title Certain Relations in Statistical Physics Based on Rényi Entropy
title_full Certain Relations in Statistical Physics Based on Rényi Entropy
title_fullStr Certain Relations in Statistical Physics Based on Rényi Entropy
title_full_unstemmed Certain Relations in Statistical Physics Based on Rényi Entropy
title_short Certain Relations in Statistical Physics Based on Rényi Entropy
title_sort certain relations in statistical physics based on rényi entropy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955908/
http://dx.doi.org/10.3103/S002713492006003X
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