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Optimal transport is a powerful framework for computing distances between probability distributions. We unify the two main approaches to optimal transport, namely Monge-Kantorovitch and Sinkhorn-Cuturi, into what we define as Tsallis…

Machine Learning · Computer Science 2016-09-16 Boris Muzellec , Richard Nock , Giorgio Patrini , Frank Nielsen

Tsallis and R\'{e}nyi entropies, which are monotone transformations of each other, are deformations of the celebrated Shannon entropy. Maximization of these deformed entropies, under suitable constraints, leads to the $q$-exponential family…

Probability · Mathematics 2022-01-14 Ting-Kam Leonard Wong , Jun Zhang

The asymptotic correspondence between the probability mass function of the $q$-deformed multinomial distribution and the $q$-generalised Kullback-Leibler divergence, also known as Tsallis relative entropy, is established. The probability…

Statistical Mechanics · Physics 2025-03-10 Keisuke Okamura

There are two kinds of Tsallis-probability distributions: heavy tail ones and compact support distributions. We show here, appealing to functional analysis' tools, that for lower bound Hamiltonians only the first type guarantees a maximum…

Statistical Mechanics · Physics 2015-11-16 A. Plastino , M. C. Rocca

Tsallis has suggested a nonextensive generalization of the Boltzmann-Gibbs entropy, the maximization of which gives a generalized canonical distribution under special constraints. In this brief report we show that the generalized canonical…

Statistical Mechanics · Physics 2021-04-28 Brian R. La Cour , William C. Schieve

We commonly encounter the problem of identifying an optimally weight adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the…

Machine Learning · Statistics 2024-01-17 Abhisek Chakraborty , Anirban Bhattacharya , Debdeep Pati

In this work, we derive information-theoretic properties for a modified Tsallis entropy, hereinafter referred to as q-entropy. We introduce the notions of joint q-entropy, conditional q-entropy, relative q-entropy, conditional mutual…

Mathematical Physics · Physics 2026-03-31 Marco A. S. Trindade

Within a framework of utmost generality, we show that the entropy maximization procedure with linear constraints uniquely leads to the Shannon-Boltzmann-Gibbs entropy. Therefore, the use of this procedure with linear constraints should not…

Statistical Mechanics · Physics 2018-05-01 Thomas Oikonomou , G. Baris Bagci

We consider the maximum entropy problems associated with R\'enyi $Q$-entropy, subject to two kinds of constraints on expected values. The constraints considered are a constraint on the standard expectation, and a constraint on the…

Information Theory · Computer Science 2008-12-18 Jean-François Bercher

Separability conditions for a bipartite quantum system of finite-dimensional subsystems are formulated in terms of R\'{e}nyi and Tsallis entropies. Entropic uncertainty relations often lead to entanglement criteria. We propose new approach…

Quantum Physics · Physics 2017-11-01 Alexey E. Rastegin

Quasi-power law ensembles are discussed from the perspective of nonextensive Tsallis distributions characterized by a nonextensive parameter $q$. A number of possible sources of such distributions are presented in more detail. It is further…

Statistical Mechanics · Physics 2023-07-19 Grzegorz Wilk , Zbigniew Włodarczyk

This paper studies the continuous-time reinforcement learning in jump-diffusion models by featuring the q-learning (the continuous-time counterpart of Q-learning) under Tsallis entropy regularization. Contrary to the Shannon entropy, the…

Optimization and Control · Mathematics 2026-02-16 Lijun Bo , Yijie Huang , Xiang Yu , Tingting Zhang

The necessary conditions (NC) that reconcile canonical probability distributions obtained from the q-maximum entropy principle, subjected to both i) the additive duality of generalized statistics and ii) normal averages expectations with…

Statistical Mechanics · Physics 2013-03-21 R. C. Venkatesan , A. Plastino

We show that distribution functions of nonequilibrium steady states (NESS) evolving under a slowly varying protocol can be accurately obtained from limited data and the closest known detailed state of the system. In this manner, one needs…

Statistical Mechanics · Physics 2015-08-26 Puneet Kumar Patra , Marc Meléndez , Baidurya Bhattacharya

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

By using the maximum entropy principle with Tsallis entropy we obtain a fragment size distribution function which undergoes a transition to scaling. This distribution function reduces to those obtained by other authors using Shannon…

Soft Condensed Matter · Physics 2015-06-24 Oscar Sotolongo-Costa , Arezky H. Rodriguez , G. J. Rodgers

We determine a general link between two different solutions of the MaxEnt variational problem, namely, the ones that correspond to using either Shannon's or Tsallis' entropies in the concomitant variational problem. It is shown that the two…

Statistics Theory · Mathematics 2015-06-03 E. Rufeil Fiori , A. Plastino

In spite of its undeniable success, there are still open questions regarding Tsallis non-extensive statistical formalism, whose founding stone was laid in 1988 in JSTAT. Some of them are concerned with the so-called normalization problem of…

Statistical Mechanics · Physics 2009-11-11 G. L. Ferri , S. Martinez , A. Plastino

A comparative study of one-dimensional quantum structures which allow analytic expressions for the position and momentum R\'{e}nyi $R(\alpha)$ and Tsallis $T(\alpha)$ entropies, focuses on extracting the most characteristic physical…

Quantum Physics · Physics 2019-01-15 O. Olendski

We calculate and analyze various entropy measures and their properties for selected probability distributions. The entropies considered include Shannon, R\'enyi, generalized R\'enyi, Tsallis, Sharma-Mittal, and modified Shannon entropy,…

Information Theory · Computer Science 2024-11-26 Iryna Bodnarchuk , Yuliya Mishura , Kostiantyn Ralchenko