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We show that starting with either the non-extensive Tsallis entropy in Wang's formalism or the extensive Renyi entropy, it is possible to construct the equilibrium statistical mechanics with non-Gibbs canonical distribution functions. The…

High Energy Physics - Phenomenology · Physics 2009-11-10 A. S. Parvan , T. S. Biro

Maximum-entropy distributions are shown to appear in the probability calculus as approximations of a model by exchangeability or a model by sufficiency, the former model being preferable. The implications of this fact are discussed,…

Data Analysis, Statistics and Probability · Physics 2017-06-27 P. G. L. Porta Mana

Tsallis entropy is a useful one-parameter generalization of the standard von Neumann entropy in information theory. We study the variance of Tsallis entropy of bipartite quantum systems in a random pure state. The main result is an exact…

Mathematical Physics · Physics 2022-02-16 Lu Wei

A simple way of obtaining robust estimates of the "center" (or the "location") and of the "scatter" of a dataset is to use the maximum likelihood estimate with a class of heavy-tailed distributions, regardless of the "true" distribution…

Statistics Theory · Mathematics 2023-11-28 Pavol Ševera

This paper is organized in three parts closely related to closure properties of heavy-tailed distributions and heavy-tailed random vectors. In the first part we consider two random variables X and Y with distributions F and G respectively.…

Probability · Mathematics 2025-02-04 Dimitrios G. Konstantinides , Charalampos D. Passalidis

We studied the effects of the Tsallis distribution on the transverse momentum fluctuation in high energy collisions. The parton-hadron duality and the Bose-Einstein type correlation between partons were assumed. The fluctuation was…

High Energy Physics - Phenomenology · Physics 2017-12-08 Masamichi Ishihara

We characterize the complex, heavy-tailed probability distribution functions (pdf) describing the response and its local extrema for structural systems subjected to random forcing that includes extreme events. Our approach is based on the…

Chaotic Dynamics · Physics 2017-06-02 Han Kyul Joo , Mustafa A. Mohamad , Themistoklis P. Sapsis

A formal correspondence between the q-distribution obtained from the Tsallis entropy and non-maxwellian distributions obtained from the Boltzmann-Gibbs entropy is afforded.

Quantum Physics · Physics 2009-11-13 N. G. de Almeida

Conventional methods for extreme event estimation rely on well-chosen parametric models asymptotically justified from extreme value theory (EVT). These methods, while powerful and theoretically grounded, could however encounter a difficult…

Methodology · Statistics 2023-01-05 Yuanlu Bai , Henry Lam , Xinyu Zhang

Within the Tsallis thermodynamics' framework, and using scaling properties of the entropy, we derive a generalization of the Gibbs-Duhem equation. The analysis suggests a transformation of variables that allows standard thermodynamics to be…

Statistical Mechanics · Physics 2009-11-07 Eduard Vives , Antoni Planes

It is argued that the factorization of compound probability over subsystems is a consequence of the existence of thermodynamic equilibrium in the composite system having Tsallis entropy. So it should be respected by all exact calculations…

Statistical Mechanics · Physics 2014-10-13 Qiuping A. Wang , Alain Le Mehaute

The paper focuses on a class of light-tailed multivariate probability distributions. These are obtained via a transformation of the margins from a heavy-tailed original distribution. This class was introduced in Balkema et al. (J.…

Probability · Mathematics 2012-11-26 Guus Balkema , Paul Embrechts , Natalia Nolde

We consider a model for multivariate data with heavy-tailed marginal distributions and a Gaussian dependence structure. The different marginals in the model are allowed to have non-identical tail behavior in contrast to most popular…

Methodology · Statistics 2023-05-23 Bikramjit Das

A broad set of sufficient conditions that guarantees the existence of the maximum entropy (maxent) distribution consistent with specified bounds on certain generalized moments is derived. Most results in the literature are either focused on…

Information Theory · Computer Science 2009-09-29 Prakash Ishwar , Pierre Moulin

Tsallis' non-extensive entropy is extended to incorporate the dependence on affinities between the microstates of a system. At the core of our construction of the extended entropy ($\mathcal{H}$) is the concept of the effective number of…

Quantitative Methods · Quantitative Biology 2022-02-08 Keisuke Okamura

The Tsallis entropy, which is a generalization of the Boltzmann-Gibbs entropy, plays a central role in nonextensive statistical mechanics of complex systems. A lot of efforts have recently been made on establishing a dynamical foundation…

Statistical Mechanics · Physics 2009-11-11 Sumiyoshi Abe , Yutaka Nakada

The aim of the paper is to show that the presence of one possible type of outliers is not connected to that of heavy tails of the distribution. In contrary, typical situation for outliers appearance is the case of compact supported…

Statistics Theory · Mathematics 2018-07-25 Lev B. Klebanov , Irina Volchenkova

A common statistical situation concerns inferring an unknown distribution Q(x) from a known distribution P(y), where X (dimension n), and Y (dimension m) have a known functional relationship. Most commonly, n<m, and the task is relatively…

Quantitative Methods · Quantitative Biology 2016-02-01 Jayajit Das , Sayak Mukherjee , Susan E. Hodge

In this paper we introduce and study several multivariate, heavy-tailed distribution classes, and we explore their closure properties and their applications. We consider the class of multivariate, positively decreasing distributions, and…

Probability · Mathematics 2026-04-28 Dimitrios G. Konstantinides , Charalampos D. Passalidis

We construct an example of a continuous centered random process with light tails of finite-dimensional distribution but with heavy tail of maximum distribution.

Probability · Mathematics 2012-08-31 E. Ostrovsky , L. Sirota
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