Comparison of Markov chains via weak Poincaré inequalities with application to pseudo-marginal MCMC
This work addresses convergence analysis for Markov chain methods in Bayesian inference, offering simpler proofs and extensions for researchers in computational statistics, though it is incremental as it builds on existing functional inequality frameworks.
The paper tackles the problem of bounding convergence of Markov chains to equilibrium, particularly for pseudo-marginal MCMC methods with intractable likelihoods, by using weak Poincaré inequalities to derive subgeometric convergence bounds and provide new insights into practical applications like ABC and PMMH.
We investigate the use of a certain class of functional inequalities known as weak Poincaré 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).