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Contrast functions play a fundamental role in information geometry, providing a means for generating the geometric structures of a statistical manifold: a pseudo-Riemannian metric and a pair of torsion-free conjugate affine connections.…

Differential Geometry · Mathematics 2025-11-11 Florio M. Ciaglia , Giuseppe Marmo , Marco Pacelli , Luca Schiavone , Alessandro Zampini

In this work we: (1) review likelihood-based inference for parameter estimation and the construction of confidence regions; and, (2) explore the use of techniques from information geometry, including geodesic curves and Riemann scalar…

Methodology · Statistics 2022-04-01 Jesse A Sharp , Alexander P Browning , Kevin Burrage , Matthew J Simpson

The recent debate on hyper-computation has raised new questions both on the computational abilities of quantum systems and the Church-Turing Thesis role in Physics. We propose here the idea of geometry of effective physical process as the…

General Physics · Physics 2010-04-26 Germano Resconi , Ignazio Licata

Classical inequalities used in information theory such as those of de Bruijn, Fisher, and Kullback carry over from the setting of probability theory on Euclidean space to that of unimodular Lie groups. These are groups that posses…

Information Theory · Computer Science 2009-06-02 Gregory S. Chirikjian

This report concerns the problem of dimensionality reduction through information geometric methods on statistical manifolds. While there has been considerable work recently presented regarding dimensionality reduction for the purposes of…

Machine Learning · Statistics 2008-09-30 Kevin M. Carter , Raviv Raich , Alfred O. Hero

In this paper, we introduce \emph{$\ell^p$-information geometry}, an infinite dimensional framework that shares key features with the geometry of the space of probability densities \( \mathrm{Dens}(M) \) on a closed manifold, while also…

Symplectic Geometry · Mathematics 2026-03-23 Levin Maier

This paper lays the foundations for a unified framework for numerically and computationally applying methods drawn from a range of currently distinct geometrical approaches to statistical modelling. In so doing, it extends information…

Statistics Theory · Mathematics 2012-09-11 Karim Anaya-Izquierdo , Frank Critchley , Paul Marriott , Paul W. Vos

It is well known that quantum mechanics admits a geometric formulation on the complex projective space as a Kahler manifold. In this paper we consider the notion of mutual information among continuous random variables in relation to the…

Mathematical Physics · Physics 2020-04-07 Davide Pastorello

The Fisher's information metric is introduced in order to find the real meaning of the probability distribution in classical and quantum systems described by Riemaniann non-degenerated superspaces. In particular, the physical r\^{o}le…

High Energy Physics - Theory · Physics 2012-12-04 Diego Julio Cirilo-Lombardo , Victor I. Afonso

The unification of general relativity with quantum theory will also require a coming together of the two quite different mathematical languages of general relativity and quantum theory, i.e., of differential geometry and functional analysis…

Mathematical Physics · Physics 2016-04-27 Mikhail Panine , Achim Kempf

Complementary idempotent paravectors and their ordered compositions, are used to represent multivector basis elements of geometric Clifford algebra for 3D Euclidean space as the states of a geometric byte in a given frame of reference. Two…

Quantum Physics · Physics 2009-11-13 Victor I. Tarkhanov , Michael M. Nesterov

In a recent paper it was shown that all the Hilbert space formulas for quantum probabilities can be realized as functions of geometric properties of the associated projective space, but those functions were expressed using the structures of…

Quantum Physics · Physics 2026-05-26 Stephen Bruce Sontz

We formulate the Riemannian calculus of the probability set embedded with $L^2$-Wasserstein metric. This is an initial work of transport information geometry. Our investigation starts with the probability simplex (probability manifold)…

Differential Geometry · Mathematics 2022-04-05 Wuchen Li

Inferring and comparing complex, multivariable probability density functions is fundamental to problems in several fields, including probabilistic learning, network theory, and data analysis. Classification and prediction are the two faces…

Information Theory · Computer Science 2017-03-30 David J. Galas , T. Gregory Dewey , James Kunert-Graf , Nikita A. Sakhanenko

We discuss new approach to mathematical foundations of quantum theory, which is completely independent of Hilbert spaces and measure spaces. New kinematics is defined by non-linear geometry of spaces of integrals on abstract non-commutative…

Quantum Physics · Physics 2011-12-05 Ryszard Paweł Kostecki

The basic methods of constructing the sets of mutually unbiased bases in the Hilbert space of an arbitrary finite dimension are discussed and an emerging link between them is outlined. It is shown that these methods employ a wide range of…

Quantum Physics · Physics 2009-11-10 Michel R. P. Planat , Haret Rosu , Serge Perrine , Metod Saniga

When dealing with a parametric statistical model, a Riemannian manifold can naturally appear by endowing the parameter space with the Fisher information metric. The geometry induced on the parameters by this metric is then referred to as…

Machine Learning · Statistics 2023-10-03 Florent Bouchard , Arnaud Breloy , Antoine Collas , Alexandre Renaux , Guillaume Ginolhac

Geometrical methods in quantum information are very promising for both providing technical tools and intuition into difficult control or optimization problems. Moreover, they are of fundamental importance in connecting pure geometrical…

Quantum Physics · Physics 2024-03-21 Daniele Iannotti , Alioscia Hamma

I review recent works showing that information geometry is a useful framework to characterize quantum coherence and entanglement. Quantum systems exhibit peculiar properties which cannot be justified by classical physics, e.g. quantum…

Quantum Physics · Physics 2018-10-08 Davide Girolami

Using the square-root map p-->\sqrt{p} a probability density function p can be represented as a point of the unit sphere S in the Hilbert space of square-integrable functions. If the density function depends smoothly on a set of parameters,…

Statistical Mechanics · Physics 2009-12-31 Dorje C. Brody , Daniel W. Hook