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We show that for any $\alpha>0$ the R\'enyi entropy of order $\alpha$ is minimized, among all symmetric log-concave random variables with fixed variance, either for a uniform distribution or for a two sided exponential distribution. The…

Information Theory · Computer Science 2021-10-05 Maciej Białobrzeski , Piotr Nayar

Many complex systems are characterized by non-Boltzmann distribution functions of their statistical variables. If one wants to -- justified or not -- hold on to the maximum entropy principle for complex statistical systems (non-Boltzmann)…

Statistical Mechanics · Physics 2009-11-13 Stefan Thurner , Rudolf Hanel

The necessary information to distinguish a local inhomogeneous mass density field from its spatial average on a compact domain of the universe can be measured by relative information entropy. The Kullback-Leibler (KL) formula arises very…

General Relativity and Quantum Cosmology · Physics 2016-04-28 Viktor G. Czinner , Filipe C. Mena

A new method is proposed for analyzing complexity and studying the information in random geometric networks using Tsallis entropy tool. Tsallis entropy of the ensemble of random geometric networks is calculated based on the components of…

Statistical Mechanics · Physics 2025-02-20 O. K. Kazemi , S. M. Taheri

Distance measures between quantum states like the trace distance and the fidelity can naturally be defined by optimizing a classical distance measure over all measurement statistics that can be obtained from the respective quantum states.…

Quantum Physics · Physics 2017-11-09 Mario Berta , Omar Fawzi , Marco Tomamichel

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 Pinsker inequality lower bounds the Kullback--Leibler divergence $D_{\textrm{KL}}$ in terms of total variation and provides a canonical way to convert $D_{\textrm{KL}}$ control into $\lVert \cdot \rVert_1$-control. Motivated by…

Information Theory · Computer Science 2026-02-06 Guglielmo Beretta , Tommaso Cesari , Roberto Colomboni

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

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

In ordinary statistical mechanics the Boltzmann-Shannon entropy is related to the Maxwell-Bolzmann distribution $p_i$ by means of a twofold link. The first link is differential and is offered by the Jaynes Maximum Entropy Principle. The…

Statistical Mechanics · Physics 2009-10-02 G. Kaniadakis

We establish a reverse inequality for Tsallis relative operator entropy involving a positive linear map. In addition, we present converse of Ando's inequality, for each parameter. We give examples to compare our results with the known…

Functional Analysis · Mathematics 2018-11-16 H. R. Moradi , S. Furuichi

The so-called $q$-z-\textit{R\'enyi Relative Entropies} provide a huge two-parameter family of relative entropies which includes almost all well-known examples of quantum relative entropies for suitable values of the parameters. In this…

The Lagrangian technique of Niven (2004, Physica A, 334(3-4): 444) is used to determine the constrained forms of the Tsallis entropy function - i.e. Lagrangian functions in which the probabilities of each state are independent - for each…

Statistical Mechanics · Physics 2007-05-23 Robert K. Niven

The relative entropy of entanglement $E_R$ is defined as the distance of a multi-partite quantum state from the set of separable states as measured by the quantum relative entropy. We show that this optimisation is always achieved, i.e. any…

Quantum Physics · Physics 2023-10-27 Ludovico Lami , Maksim E. Shirokov

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

We introduce hardness in relative entropy, a new notion of hardness for search problems which on the one hand is satisfied by all one-way functions and on the other hand implies both next-block pseudoentropy and inaccessible entropy, two…

Cryptography and Security · Computer Science 2024-11-15 Rohit Agrawal , Yi-Hsiu Chen , Thibaut Horel , Salil Vadhan

The exponential family is well known in machine learning and statistical physics as the maximum entropy distribution subject to a set of observed constraints, while the geometric mixture path is common in MCMC methods such as annealed…

Machine Learning · Computer Science 2021-01-18 Rob Brekelmans , Frank Nielsen , Alireza Makhzani , Aram Galstyan , Greg Ver Steeg

We calculate the limiting behavior of relative Renyi entropy when the first probability distribution is close to the second one in a non-regular location-shift family which is generated by a probability distribution whose support is an…

Probability · Mathematics 2007-06-13 Masahito Hayashi

We generalize the family of $\alpha$-divergences using a pair of strictly comparable weighted means. In particular, we obtain the $1$-divergence in the limit case $\alpha\rightarrow 1$ (a generalization of the Kullback-Leibler divergence)…

Information Theory · Computer Science 2022-11-23 Frank Nielsen

We revisit the derivation of a formula for the $q$-generalised multinomial coefficient rooted in the $q$-deformed algebra, a foundational framework in the study of nonextensive statistics. Previous approximate expressions in the literature…

Statistical Mechanics · Physics 2024-10-08 Keisuke Okamura
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