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Related papers: Nonextensive information theoretical machine

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Most of the existing classification methods are aimed at minimization of empirical risk (through some simple point-based error measured with loss function) with added regularization. We propose to approach this problem in a more information…

Machine Learning · Computer Science 2015-01-22 Wojciech Marian Czarnecki , Jacek Tabor

In this paper we introduce a nonextensive quantum information theoretic measure which may be defined between any arbitrary number of density matrices, and we analyze its fundamental properties in the spectral graph-theoretic framework.…

Information Theory · Computer Science 2015-04-15 A. Ben Hamza

Entropy and relative or cross entropy measures are two very fundamental concepts in information theory and are also widely used for statistical inference across disciplines. The related optimization problems, in particular the maximization…

Statistics Theory · Mathematics 2021-06-18 Abhik Ghosh , Ayanendranath Basu

The framework of non-extensive statistical mechanics, proposed by Tsallis, has been used to describe a variety of systems. The non-extensive statistical mechanics is usually introduced in a formal way, thus simple models exhibiting some…

Statistical Mechanics · Physics 2014-08-06 Julius Ruseckas

Based on the Tsallis entropy, the nonextensive thermodynamic properties are studied as a q-deformation of classical statistical results using only probabilistic methods and straightforward calculations. It is shown that the constant in the…

Statistical Mechanics · Physics 2007-05-23 Franck Jedrzejewski

By replacing linear averaging in Shannon entropy with Kolmogorov-Nagumo average (KN-averages) or quasilinear mean and further imposing the additivity constraint, R\'{e}nyi proposed the first formal generalization of Shannon entropy. Using…

Information Theory · Computer Science 2007-07-13 Ambedkar Dukkipati , M. Narasimha Murty , Shalabh Bhatnagar

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

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

Gibbs-Boltzmann entropy leads to systems that have a strong dependence on initial conditions. In reality, most materials behave quite independently of initial conditions. Nonextensive entropy or Tsallis entropy leads to nonextensive…

Statistical Mechanics · Physics 2022-07-01 Saman Amiri , Mahdi Mirzaee , Mohammad Mazhari

The properties of the nonextensive parameter q and the Tsallis distribution for self-gravitating systems are studied. A mathematical expression of q is deduced based on the generalized Boltzmann equation, the q-H theorem and the generalized…

Statistical Mechanics · Physics 2015-08-10 Jiulin Du

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

In this paper, we develop the lower and upper bounds of worst-case distortion riskmetrics and weighted entropy for unimodal, and symmetric unimodal distributions when mean and variance information are available. We also consider the sharp…

Risk Management · Quantitative Finance 2025-11-24 Baishuai Zuo , Chuancun Yin

Quite general, analytical (both exact and approximate) forms for discrete probability distributions (PD's) that maximize Tsallis entropy for a fixed variance are here investigated. They apply, for instance, in a wide variety of scenarios in…

Statistical Mechanics · Physics 2009-11-11 C. Vignat , A. Plastino

Shannon information entropy is a natural measure of probability (de)localization and thus (un)predictability in various procedures of data analysis for model systems. We pay particular attention to links between the Shannon entropy and the…

Statistical Mechanics · Physics 2007-05-23 Piotr Garbaczewski

We introduce a variational algorithm based on Matrix Product States that is trained by minimizing a generalized free energy defined using Tsallis entropy instead of the standard Gibbs entropy. As a result, our model can generate the…

Statistical Mechanics · Physics 2024-09-16 Pablo Díez-Valle , Fernando Martínez-García , Juan José García-Ripoll , Diego Porras

We find the value of constants related to constraints in characterization of some known statistical distributions and then we proceed to use the idea behind maximum entropy principle to derive generalized version of this distributions using…

Statistical Mechanics · Physics 2007-05-23 Oscar Sotolongo-Costa , Alejandro Gonzalez Gonzalez , Francois Brouers

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

We consider a possible generalization of the random matrix theory, which involves the maximization of Tsallis' $q$-parametrized entropy. We discuss the dependence of the spacing distribution on $q$ using a non-extensive generalization of…

Statistical Mechanics · Physics 2007-05-23 A. Y. Abul-Magd

We propose communication-efficient distributed estimation and inference methods for the transelliptical graphical model, a semiparametric extension of the elliptical distribution in the high dimensional regime. In detail, the proposed…

Machine Learning · Statistics 2016-12-30 Pan Xu , Lu Tian , Quanquan Gu

A general theory for computing information transfers in nonlinear stochastic systems driven by deterministic forcing and additive and/or multiplicative noises, is presented. It extends the Liang-Kleeman framework of causality inference to…

Chaotic Dynamics · Physics 2023-10-12 Carlos A. Pires , David Docquier , Stéphane Vannitsem