Entropy Optimization, Maxwell–Boltzmann, and Rayleigh Distributions

In physics, communication theory, engineering, statistics, and other areas, one of the methods of deriving distributions is the optimization of an appropriate measure of entropy under relevant constraints. In this paper, it is shown that by optimizing a measure of entropy introduced by the second au...

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Main Authors: Nicy Sebastian, Arak M. Mathai, Hans J. Haubold
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Entropy
Subjects:
Online Access:https://www.mdpi.com/1099-4300/23/6/754
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author Nicy Sebastian
Arak M. Mathai
Hans J. Haubold
author_facet Nicy Sebastian
Arak M. Mathai
Hans J. Haubold
author_sort Nicy Sebastian
collection DOAJ
description In physics, communication theory, engineering, statistics, and other areas, one of the methods of deriving distributions is the optimization of an appropriate measure of entropy under relevant constraints. In this paper, it is shown that by optimizing a measure of entropy introduced by the second author, one can derive densities of univariate, multivariate, and matrix-variate distributions in the real, as well as complex, domain. Several such scalar, multivariate, and matrix-variate distributions are derived. These include multivariate and matrix-variate Maxwell–Boltzmann and Rayleigh densities in the real and complex domains, multivariate Student-t, Cauchy, matrix-variate type-1 beta, type-2 beta, and gamma densities and their generalizations.
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spelling doaj.art-4f0be6510d5f486994f18adfd801da0a2023-11-22T00:13:50ZengMDPI AGEntropy1099-43002021-06-0123675410.3390/e23060754Entropy Optimization, Maxwell–Boltzmann, and Rayleigh DistributionsNicy Sebastian0Arak M. Mathai1Hans J. Haubold2Department of Statistics, St. Thomas College, Thrissur, Kerala 680001, IndiaDepartment of Mathematics and Statistics, McGill University, Montreal, QC H0H H9X, CanadaOffice for Outer Space Affairs, United Nations, Vienna International Center, A-1400 Vienna, AustriaIn physics, communication theory, engineering, statistics, and other areas, one of the methods of deriving distributions is the optimization of an appropriate measure of entropy under relevant constraints. In this paper, it is shown that by optimizing a measure of entropy introduced by the second author, one can derive densities of univariate, multivariate, and matrix-variate distributions in the real, as well as complex, domain. Several such scalar, multivariate, and matrix-variate distributions are derived. These include multivariate and matrix-variate Maxwell–Boltzmann and Rayleigh densities in the real and complex domains, multivariate Student-t, Cauchy, matrix-variate type-1 beta, type-2 beta, and gamma densities and their generalizations.https://www.mdpi.com/1099-4300/23/6/754complex Maxwell–Boltzmann and Rayleigh densitiesmultivariate and matrix-variate densitiesmatrix-variate pathway modelstype-1, type-2 beta densitiesgeneralized gammageneralized entropy
spellingShingle Nicy Sebastian
Arak M. Mathai
Hans J. Haubold
Entropy Optimization, Maxwell–Boltzmann, and Rayleigh Distributions
Entropy
complex Maxwell–Boltzmann and Rayleigh densities
multivariate and matrix-variate densities
matrix-variate pathway models
type-1, type-2 beta densities
generalized gamma
generalized entropy
title Entropy Optimization, Maxwell–Boltzmann, and Rayleigh Distributions
title_full Entropy Optimization, Maxwell–Boltzmann, and Rayleigh Distributions
title_fullStr Entropy Optimization, Maxwell–Boltzmann, and Rayleigh Distributions
title_full_unstemmed Entropy Optimization, Maxwell–Boltzmann, and Rayleigh Distributions
title_short Entropy Optimization, Maxwell–Boltzmann, and Rayleigh Distributions
title_sort entropy optimization maxwell boltzmann and rayleigh distributions
topic complex Maxwell–Boltzmann and Rayleigh densities
multivariate and matrix-variate densities
matrix-variate pathway models
type-1, type-2 beta densities
generalized gamma
generalized entropy
url https://www.mdpi.com/1099-4300/23/6/754
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