Explicit Constraints on the Geometric Rate of Convergence of Random Walk Metropolis-Hastings

TL;DR

This paper develops explicit drift, minorization, and spectral bounds to precisely quantify the geometric convergence rate of random walk Metropolis-Hastings algorithms, extending analysis to complex Bayesian models.

Contribution

It introduces explicit constraints on the geometric convergence rate of random walk Metropolis-Hastings, enabling analysis in new complex Bayesian settings.

Findings

Explicit upper and lower bounds on convergence rate

Application to Bayesian Poisson regression and generalized linear models

Extension of conditions for geometric ergodicity

Abstract

Convergence rate analyses of random walk Metropolis-Hastings Markov chains on general state spaces have largely focused on establishing sufficient conditions for geometric ergodicity or on analysis of mixing times. Geometric ergodicity is a key sufficient condition for the Markov chain Central Limit Theorem and allows rigorous approaches to assessing Monte Carlo error. The sufficient conditions for geometric ergodicity of the random walk Metropolis-Hastings Markov chain are refined and extended, which allows the analysis of previously inaccessible settings such as Bayesian Poisson regression. The key technical innovation is the development of explicit drift and minorization conditions for random walk Metropolis-Hastings, which allows explicit upper and lower bounds on the geometric rate of convergence. Further, lower bounds on the geometric rate of convergence are also developed using spectral theory. The existing sufficient conditions for geometric ergodicity, to date, have not provided explicit constraints on the rate of geometric rate of convergence because the method used only implies the existence of drift and minorization conditions. The theoretical results are applied to random walk Metropolis-Hastings algorithms for a class of exponential families and generalized linear models that address Bayesian Regression problems.