Effective limit theorems for Markov chains with a spectral gap

TL;DR

This paper establishes non-asymptotic limit theorems for Markov chains with a spectral gap in various Banach spaces, providing explicit constants and broad applicability, especially in MCMC methods.

Contribution

It introduces a spectral gap framework in Banach algebras for Markov chains, yielding concentration and Berry-Esseen bounds without reversibility or warm start assumptions.

Findings

Derived explicit concentration inequalities.

Established Berry-Esseen bounds for Markov chains.

Applied results to examples like uniform ergodicity and Bernoulli convolutions.

Abstract

Applying quantitative perturbation theory for linear operators, we prove non-asymptotic limit theorems for Markov chains whose transition kernel has a spectral gap in an arbitrary Banach algebra of functions X . The main results are concentration inequalities and Berry-Esseen bounds, obtained assuming neither reversibility nor `warm start' hypothesis: the law of the first term of the chain can be arbitrary. The spectral gap hypothesis is basically a uniform X-ergodicity hypothesis, and when X consist in regular functions this is weaker than uniform ergodicity. We show on a few examples how the flexibility in the choice of function space can be used. The constants are completely explicit and reasonable enough to make the results usable in practice, notably in MCMC methods.v2: Introduction rewritten, Section 3 applying the main results to examples improved (uniformly ergodic chains and Bernoulli convolutions have been notably added) . Main results and their proofs are unchanged.