Finitude of physical measures for Markovian random maps
Pablo G. Barrientos, Dominique Malicet, Fumihiko Nakamura, Yushi Nakano, Hisayoshi Toyokawa

TL;DR
This paper investigates conditions under which Markovian random dynamical systems have finitely many physical measures, extending results from i.i.d. systems to Markov chains, and applies these to specific classes of random maps.
Contribution
It establishes criteria for the finiteness of physical measures in Markov-driven systems using an i.i.d. representation and explores their properties in various dynamical contexts.
Findings
Finiteness of physical measures is guaranteed under certain contraction conditions.
Results extend Bernoulli system findings to Markovian systems.
Application to random diffeomorphisms on the circle and interval.
Abstract
We study the finiteness of physical measures for skew-product transformations associated with discrete-time random dynamical systems driven by ergodic Markov chains. We develop a framework, using an independent and identically distributed (i.i.d.) representation of the Markov process, that facilitates transferring results from the well-studied Bernoulli (i.i.d.) setting to the Markovian context. Specifically, we establish conditions for the existence of finitely many ergodic, -invariant measures, absolutely continuous with respect to a reference measure, such that their statistical basins of attraction for measurable bounded observables cover the phase space almost everywhere. Furthermore, we investigate a weaker notion, which demands finitely many physical measures (not necessarily absolutely continuous) whose weak basins of attraction cover the phase space almost…
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Taxonomy
TopicsStochastic processes and statistical mechanics · Mathematical Dynamics and Fractals · Markov Chains and Monte Carlo Methods
