Computing Invariant Measures of Weakly Convex Random Maps with Position Dependent Probabilities

Abstract

A random map is closely related to a chaotic system consisting of a collection of measurable transformations. For a class of weakly convex random maps, we propose piecewise linear or quadratic Markov methods for numerically computing an absolutely continuous invariant measure and prove their convergence.