Maps on statistical manifolds exactly reduced from the Perron-Frobenius equations for solvable chaotic maps

Abstract

Maps on a parameter space for expressing distribution functions are exactly derived from the Perron-Frobenius equations for a generalized Boole transform family. Here the generalized Boole transform family is a one-parameter family of maps where it is defined on a subset of the real line and its probability distribution function is the Cauchy distribution with some parameters. With this reduction, some relations between the statistical picture and the orbital one are shown. From the viewpoint of information geometry, the parameter space can be identified with a statistical manifold, and then it is shown that the derived maps can be characterized. Also, with an induced symplectic structure from a statistical structure, symplectic and information geometric aspects of the derived maps are discussed.

Hessian-information geometric formulation of a class of deterministic neural network models

“…In the literature, several dynamical systems have been studied in dually flat spaces [14,15,9]. In the so-called statistical manifolds, which are manifolds generalized from dually flat spaces, several dynamical systems theories have been considered [17,18,19]. This Lemma will be used to calculate the phase space compressibility.…”