Comparison of Markov chains via weak Poincar\'e inequalities with application to pseudo-marginal MCMC

TL;DR

This paper uses weak Poincaré inequalities to derive subgeometric convergence bounds for Markov chains, providing new insights into pseudo-marginal MCMC methods and simplifying analysis compared to traditional approaches.

Contribution

It introduces novel comparison theorems for Markov chains using weak Poincaré inequalities, extending and simplifying convergence analysis for pseudo-marginal algorithms.

Findings

Derived subgeometric convergence bounds for pseudo-marginal methods.

Provided new insights into the effect of averaging in ABC.

Analyzed lognormal weights in PMMH.

Abstract

We investigate the use of a certain class of functional inequalities known as weak Poincar\'e inequalities to bound convergence of Markov chains to equilibrium. We show that this enables the straightforward and transparent derivation of subgeometric convergence bounds for methods such as the Independent Metropolis--Hastings sampler and pseudo-marginal methods for intractable likelihoods, the latter being subgeometric in many practical settings. These results rely on novel quantitative comparison theorems between Markov chains. Associated proofs are simpler than those relying on drift/minorization conditions and the tools developed allow us to recover and further extend known results as particular cases. We are then able to provide new insights into the practical use of pseudo-marginal algorithms, analyse the effect of averaging in Approximate Bayesian Computation (ABC) and the use of products of independent averages, and also to study the case of lognormal weights relevant to particle marginal Metropolis--Hastings (PMMH).