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Exponential varieties arise from exponential families in statistics. These real algebraic varieties have strong positivity and convexity properties, familiar from toric varieties and their moment maps. Among them are varieties of inverses…

Algebraic Geometry · Mathematics 2017-05-17 Mateusz Michałek , Bernd Sturmfels , Caroline Uhler , Piotr Zwiernik

We examine the role of information geometry in the context of classical Cram\'er-Rao (CR) type inequalities. In particular, we focus on Eguchi's theory of obtaining dualistic geometric structures from a divergence function and then applying…

Information Theory · Computer Science 2021-08-24 Kumar Vijay Mishra , M. Ashok Kumar

Exponential families comprise a broad class of statistical models and parametric families like normal distributions, binomial distributions, gamma distributions or exponential distributions. Thereby the formal representation of its…

Statistics Theory · Mathematics 2020-06-23 Patrick Michl

We derive a closed form solution for the Kullback-Leibler divergence between two generalized gamma distributions. These notes are meant as a reference and provide a guided tour towards a result of practical interest that is rarely…

Information Theory · Computer Science 2014-01-28 Christian Bauckhage

We define a three-parameter deformation of the Weyl-Heisenberg algebra that generalizes the q-oscillator algebra. By a purely algebraical procedure, we set up on this quantum space two differential calculi that are shown to be invariant on…

q-alg · Mathematics 2009-10-30 M. Irac-Astaud

Density-based directed distances -- particularly known as divergences -- between probability distributions are widely used in statistics as well as in the adjacent research fields of information theory, artificial intelligence and machine…

Statistics Theory · Mathematics 2022-03-03 Michel Broniatowski , Wolfgang Stummer

We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors and proceeds using a simple…

Machine Learning · Computer Science 2020-07-14 Sebastian Claici , Mikhail Yurochkin , Soumya Ghosh , Justin Solomon

There are many information and divergence measures exist in the literature on information theory and statistics. The most famous among them are Kullback-Leiber relative information and Jeffreys J-divergence. The measures like, Bhattacharya…

Probability · Mathematics 2007-05-23 Inder Jeet Taneja

We propose an algebraic geometric stability criterion for a polarised variety to admit an extremal Kaehler metric. This generalises conjectures by Yau, Tian and Donaldson which relate to the case of Kaehler-Einstein and constant scalar…

Algebraic Geometry · Mathematics 2007-05-23 Gábor Székelyhidi

We derive a new variational formula for the R\'enyi family of divergences, $R_\alpha(Q\|P)$, between probability measures $Q$ and $P$. Our result generalizes the classical Donsker-Varadhan variational formula for the Kullback-Leibler…

Machine Learning · Statistics 2021-07-21 Jeremiah Birrell , Paul Dupuis , Markos A. Katsoulakis , Luc Rey-Bellet , Jie Wang

The variation distance closure of an exponential family with a convex set of canonical parameters is described, assuming no regularity conditions. The tools are the concepts of convex core of a measure and extension of an exponential…

Probability · Mathematics 2007-05-23 Imre Csiszar , Frantisek Matus

In this paper, we give a method to construct "good" exponential families systematically by representation theory. More precisely, we consider a homogeneous space $G/H$ as a sample space and construct an exponential family invariant under…

Statistics Theory · Mathematics 2022-10-14 Koichi Tojo , Taro Yoshino

Taylor's law, also known as fluctuation scaling in physics and the power-law variance function in statistics, is an empirical pattern widely observed across fields including ecology, physics, finance, and epidemiology. It states that the…

Statistics Theory · Mathematics 2025-10-13 Pok Him Cheng , Joel E. Cohen , Hok Kan Ling , Sheung Chi Phillip Yam

We study quasi-Newton methods from the viewpoint of information geometry induced associated with Bregman divergences. Fletcher has studied a variational problem which derives the approximate Hessian update formula of the quasi-Newton…

Computation · Statistics 2010-10-15 Takafumi Kanamori , Atsumi Ohara

We prove that the $f$-divergences between univariate Cauchy distributions are all symmetric, and can be expressed as strictly increasing scalar functions of the symmetric chi-squared divergence. We report the corresponding scalar functions…

Information Theory · Computer Science 2022-12-26 Frank Nielsen , Kazuki Okamura

Estimating the Kullback-Leibler (KL) divergence between two distributions given samples from them is well-studied in machine learning and information theory. Motivated by considerations of multi-group fairness, we seek KL divergence…

Machine Learning · Computer Science 2022-03-01 Parikshit Gopalan , Nina Narodytska , Omer Reingold , Vatsal Sharan , Udi Wieder

We formulate a Herglotz-type variational principle on a Lie algebroid and derive the corresponding Euler--Lagrange--Herglotz equations for a Lagrangian depending on an additional scalar variable $z$. This provides a geometric framework for…

Mathematical Physics · Physics 2025-12-22 Alexandre Anahory Simoes , Leonardo Colombo

The current article continues our project on representation theory, Euler elements, causal homogeneous spaces and Algebraic Quantum Field Theory (AQFT). We call a pair (h,k) of Euler elements orthogonal if $e^{\pi i \ad h} k = -k$. We show…

Representation Theory · Mathematics 2025-08-18 Vincenzo Morinelli , Karl-Hermann Neeb , Gestur Olafsson

We show that in addition to well known Bessel, Hermite-Gauss, and Laguerre-Gauss beams of electromagnetic radiation, one may also construct exponential beams. These beams are characterized by a fall-off in the transverse direction described…

Optics · Physics 2009-11-13 Iwo Bialynicki-Birula , Zofia Bialynicka-Birula

In this paper, some new upper bounds for Kullback-Leibler divergence(KL-divergence) based on $L^1, L^2$ and $L^\infty$ norms of density functions are discussed. Our findings unveil that the convergence in KL-divergence sense sandwiches…

Probability · Mathematics 2024-10-31 Liuquan Yao , Songhao Liu