paper

Curvature of fluctuation geometry and its implications on Riemannian fluctuation theory

arXiv:1307.7762 · doi:10.1088/1751-8113/46/34/345003

Abstract

Fluctuation geometry was recently proposed as a counterpart approach of Riemannian geometry of inference theory. This theory describes the geometric features of the statistical manifold of random events that are described by a family of continuous distributions . A main goal of this work is to clarify the statistical relevance of Levi-Civita curvature tensor of the statistical manifold . For this purpose, the notion of \emph{irreducible statistical correlations} is introduced. Specifically, a distribution exhibits irreducible statistical correlations if every distribution obtained from by considering a coordinate change cannot be factorized into independent distributions as . It is shown that the curvature tensor arises as a direct indicator about the existence of irreducible statistical correlations. Moreover, the curvature scalar allows to introduce a criterium for the applicability of the \emph{gaussian approximation} of a given distribution function. This type of asymptotic result is obtained in the framework of the second-order geometric expansion of the distributions family , which appears as a counterpart development of the high-order asymptotic theory of statistical estimation. In physics, fluctuation geometry represents the mathematical apparatus of a Riemannian extension for Einstein's fluctuation theory of statistical mechanics. Some exact results of fluctuation geometry are now employed to derive the \emph{invariant fluctuation theorems}.

Version accepted for publication in Journal of Physics A: Mathematical and Theoretical

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