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The relative $\alpha$-entropy is the R\'enyi analog of relative entropy and arises prominently in information-theoretic problems. Recent information geometric investigations on this quantity have enabled the generalization of the…

Information Theory · Computer Science 2020-02-13 Kumar Vijay Mishra , M. Ashok Kumar

We develop a fractional extension of the classical binomial distribution and the associated Bernstein operator, formulated within the framework of the generalized binomial theorem (Hara and Hino [Bull.\ London Math.\ Soc. \textbf{42}…

Probability · Mathematics 2026-02-26 Masanori Hino , Ryuya Namba

In this paper we propose a family of multivariate asymmetric distributions over an arbitrary subset of set of real numbers which is defined in terms of the well-known elliptically symmetric distributions. We explore essential properties,…

Methodology · Statistics 2024-09-02 Roberto Vila , Helton Saulo , Leonardo Santos , João Monteiros , Felipe Quintino

Common statistical measures of uncertainty such as $p$-values and confidence intervals quantify the uncertainty due to sampling, that is, the uncertainty due to not observing the full population. However, sampling is not the only source of…

Methodology · Statistics 2024-07-08 Suyash Gupta , Dominik Rothenhäusler

The Fisher-Rao distance is the geodesic distance between probability distributions in a statistical manifold equipped with the Fisher metric, which is a natural choice of Riemannian metric on such manifolds. It has recently been applied to…

Statistics Theory · Mathematics 2024-09-25 Henrique K. Miyamoto , Fábio C. C. Meneghetti , Julianna Pinele , Sueli I. R. Costa

A nonextensive thermostatic approach to chaotic dynamical systems is developed by expressing generalized Tsallis distribution as escort distribution. We explicitly show the thermodynamic limit and also derive the Legendre Transform…

Statistical Mechanics · Physics 2009-10-31 Ramandeep S. Johal , Renuka Rai

In this paper, we study the statistical and geometrical properties of the Kullback-Leibler divergence with kernel covariance operators (KKL) introduced by Bach [2022]. Unlike the classical Kullback-Leibler (KL) divergence that involves…

Machine Learning · Statistics 2025-03-12 Clémentine Chazal , Anna Korba , Francis Bach

The relevance of the concept of Fisher information is increasing in both statistical physics and quantum computing. From a statistical mechanical standpoint, the application of Fisher information in the kinetic theory of gases is…

Quantum Physics · Physics 2018-05-09 Carlo Cafaro , Paul M. Alsing

This paper explores the possibility of establishing an analytic form of the distribution of the order parameter fluctuations in a two-dimensional critical spin wave model, or width fluctuations of a two dimensional Edwards-Wilkinson…

Statistical Mechanics · Physics 2022-04-13 Steven T. Bramwell

We propose an information-geometric signature of nonconservative driving that detects violations of detailed balance using the Kullback--Leibler divergence and the Fisher information. For Markov jump processes satisfying detailed balance,…

Statistical Mechanics · Physics 2026-05-06 Andrea Auconi , Sosuke Ito

The scaling theory of Anderson localization is based on a global conductance $g_L$ that remains a random variable of order O(1) at criticality. One realization of such a conductance is the Landauer transmission for many transverse channels.…

Disordered Systems and Neural Networks · Physics 2009-07-21 Cecile Monthus , Thomas Garel

The space of probability distributions on a given sample space possesses natural geometric properties. For example, in the case of a smooth parametric family of probability distributions on the real line, the parameter space has a…

General Finance · Quantitative Finance 2015-06-03 Dorje C. Brody , Lane P. Hughston

We consider the problem of sampling from a probability distribution $\pi$. It is well known that this can be written as an optimisation problem over the space of probability distributions in which we aim to minimise the Kullback--Leibler…

Machine Learning · Statistics 2026-04-23 Francesca Romana Crucinio , Sahani Pathiraja

We recently proposed a general algorithm for approximating nonstandard Bayesian posterior distributions by minimization of their Kullback-Leibler divergence with respect to a more convenient approximating distribution. In this note we offer…

Computation · Statistics 2014-01-10 Tim Salimans

Closed-form expressions for the distributions of the order statistics on the spacings between order statistics for the uniform distribution are obtained. This generalizes a result by Fisher concerning tests of significance in the harmonic…

Statistics Theory · Mathematics 2019-09-17 Iosif Pinelis

An invariant ensemble of $N\times N$ random matrices can be characterised by a joint distribution for eigenvalues $P(\lambda_1,\cdots,\lambda_N)$. The study of the distribution of linear statistics, i.e. of quantities of the form…

Statistical Mechanics · Physics 2017-09-25 Aurélien Grabsch , Christophe Texier

In this paper, first, we survey the concept of diffeological Fisher metric and its naturality, using functorial language of probability morphisms, and slightly extending L\^e's theory in \cite{Le2020} to include weakly $C^k$-diffeological…

Statistics Theory · Mathematics 2021-07-01 Hông Vân Lê , Alexey A. Tuzhilin

The characteristic function of the folded normal distribution and its moment function are derived. The entropy of the folded normal distribution and the Kullback--Leibler from the normal and half normal distributions are approximated using…

Methodology · Statistics 2014-02-17 Michail Tsagris , Christina Beneki , Hossein Hassani

We examine the total mixed scalar curvature of a fixed distribution as a functional of a pseudo-Riemannian metric. We develop variational formulas for quantities of extrinsic geometry of the distribution to find the critical points of this…

Differential Geometry · Mathematics 2016-09-30 Vladimir Rovenski , Tomasz Zawadzki

$f$-divergences, which quantify discrepancy between probability distributions, are ubiquitous in information theory, machine learning, and statistics. While there are numerous methods for estimating $f$-divergences from data, a limit…

Statistics Theory · Mathematics 2023-10-13 Sreejith Sreekumar , Ziv Goldfeld , Kengo Kato