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Related papers: A Comment on the Tsallis Maximum Entropy Principle

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We examine the minimization of information entropy for measures on the phase space of bounded domains, subject to constraints that are averages of grand canonical distributions. We describe the set of all such constraints and show that it…

Mathematical Physics · Physics 2019-10-02 Stamatis Dostoglou , Alexander Hughes , Jianfei Xue

The Boltzmann--Gibbs entropy is a functional on the space of probability measures. When a state space is countable, one characterization of the Boltzmann--Gibbs entropy is given by the Shannon--Khinchin axioms, which consist of continuity,…

Mathematical Physics · Physics 2021-11-03 Asuka Takatsu

There is a conception that Boltzmann-Gibbs statistics cannot yield the long tail distribution. This is the justification for the intensive research of nonextensive entropies (i.e. Tsallis entropy and others). Here the error that caused this…

Information Theory · Computer Science 2009-06-30 Oded Kafri

Some preliminary evidence suggests the conjecture that the collective behaviour of systems having long-range interactions may be described more effectively by the Tsallis rather than by the Boltzmann/Gibbs/Shannon entropy. To this end, we…

Statistical Mechanics · Physics 2017-05-19 Nikos Kalogeropoulos

Gauss' law of error is generalized in Tsallis statistics such as multifractal systems, in which Tsallis entropy plays an essential role instead of Shannon entropy. For the generalization, we apply the new multiplication operation determined…

Statistical Mechanics · Physics 2007-05-23 Hiroki Suyari , Makoto Tsukada

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

The present paper studies a large class of temperature dependent probability distributions and shows that entropy and energy can be defined in such a way that these probability distributions are the equilibrium states of a generalized…

Statistical Mechanics · Physics 2015-06-24 Jan Naudts

The nonextensive statistical ensembles are revisited for the complex systems with long-range interactions and long-range correlations. An approximation, the value of nonextensive parameter (1-q) is assumed to be very tiny, is adopted for…

Statistical Mechanics · Physics 2020-02-26 Yahui Zheng , Jiulin Du , Linxia Liu , Huijun Kong

It is shown that the distribution derived from the principle of maximum Tsallis entropy is a superposable Levy-type distribution. Concomitantly, the leading order correction to the limit distribution is also deduced. This demonstration…

Statistical Mechanics · Physics 2007-05-23 Sumiyoshi Abe , A. K. Rajagopal

Recently, a new measure of information called extropy has been introduced by Lad, Sanfilippo and Agr\`o as the dual version of Shannon entropy. In the literature, Tsallis introduced a measure for a discrete random variable, named Tsallis…

Probability · Mathematics 2021-09-30 Narayanaswamy Balakrishnan , Francesco Buono , Maria Longobardi

A well-known result across information theory, machine learning, and statistical physics shows that the maximum entropy distribution under a mean constraint has an exponential form called the Gibbs-Boltzmann distribution. This is used for…

Machine Learning · Computer Science 2020-06-26 Amir R. Asadi , Emmanuel Abbe

We propose a new way of defining entropy of a system, which gives a general form which may be nonextensive as Tsallis entropy, but is linearly dependent on component entropies, like Renyi entropy, which is extensive. This entropy has a…

Adaptation and Self-Organizing Systems · Physics 2007-10-11 Fariel Shafee

The coupled entropy is proven to correct a flaw in the derivation of the Tsallis entropy and thereby solidify the theoretical foundations for analyzing the uncertainty of complex systems. The Tsallis entropy originated from considering…

Machine Learning · Statistics 2025-11-25 Kenric P. Nelson

The canonical ensemble describes an open system in equilibrium with a heat bath of fixed temperature. The probability distribution of such a system, the Boltzmann distribution, is derived from the uniform probability distribution of the…

Statistical Mechanics · Physics 2015-06-05 Julian Lee

We revisit the cut-off prescriptions which are needed in order to specify completely the form of Tsallis' maximum entropy distributions. For values of the Tsallis entropic parameter $q>1$ we advance an alternative cut-off prescription and…

Statistical Mechanics · Physics 2009-11-11 A. M. Teweldeberhan , A. R. Plastino , H. G. Miller

The notion of entropy penetrates much of science. A key feature of the all-important notion of Boltzmann-Gibbs-Shannon entropy is its extensivity (additivity over independent subsystems). However, there is a need for other quantities. In…

Mathematical Physics · Physics 2008-07-29 Flemming Topsoe

Different quantities that go by the name of entropy are used in variational principles to infer probability distributions from limited data. Shore and Johnson showed that maximizing the Boltzmann- Gibbs form of the entropy ensures that…

Statistical Mechanics · Physics 2015-06-18 Steve Pressé , Kingshuk Ghosh , Julian Lee , Ken A. Dill

We prove that information-theoretic maximum entropy (MaxEnt) approach to canonical ensemble is mathematically equivalent to the classic approach of Boltzmann, Gibbs and Darwin-Fowler. The two approaches, however, "interpret" a same…

Statistical Mechanics · Physics 2011-06-01 Hao Ge , Hong Qian

Tsallis has proposed a generalisation of the standard entropy, which has since been applied to a variety of physical systems. In the canonical ensemble approach that is mostly used, average energy is given by an unnromalised, or normalised,…

Statistical Mechanics · Physics 2009-10-31 S. Kalyana Rama

We consider the problem of guessing the realization of a random variable but under more general Tsallis' non-extensive entropic framework rather than the classical Maxwell-Boltzman-Gibbs-Shannon framework. We consider both the conditional…

Information Theory · Computer Science 2019-05-21 Abhik Ghosh