Yet another look at Harris' ergodic theorem for Markov chains
Martin Hairer, Jonathan C. Mattingly

TL;DR
This paper presents a simple, elementary proof of a variation of Harris' ergodic theorem for Markov chains, emphasizing a direct approach using weighted norms and extending classical ideas to unbounded state spaces.
Contribution
It introduces a new, straightforward proof method for Harris' ergodic theorem, avoiding complex excursion analysis and Poisson equations, with applications to spectral gaps in Wasserstein metrics.
Findings
Provides an elementary proof of Harris' ergodic theorem
Establishes spectral gaps using weighted norms
Extends classical results to unbounded state spaces
Abstract
The aim of this note is to present an elementary proof of a variation of Harris' ergodic theorem of Markov chains. This theorem, dating back to the fifties essentially states that a Markov chain is uniquely ergodic if it admits a ``small'' set which is visited infinitely often. This gives an extension of the ideas of Doeblin to the unbounded state space setting. Often this is established by finding a Lyapunov function with ``small'' level sets. This topic has been studied by many authors (cf. Harris, Hasminskii, Nummelin, Meyn and Tweedie). If the Lyapunov function is strong enough, one has a spectral gap in a weighted supremum norm (cf. Meyn and Tweedie). Traditional proofs of this result rely on the decomposition of the Markov chain into excursions away from the small set and a careful analysis of the exponential tail of the length of these excursions. There have been other…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Advanced Graph Theory Research · Complexity and Algorithms in Graphs
