Component-Wise Markov Chain Monte Carlo: Uniform and Geometric Ergodicity under Mixing and Composition

Abstract

It is common practice in Markov chain Monte Carlo to update the simulation one variable (or sub-block of variables) at a time, rather than conduct a single full-dimensional update. When it is possible to draw from each full-conditional distribution associated with the target this is just a Gibbs sampler. Often at least one of the Gibbs updates is replaced with a Metropolis-Hastings step, yielding a Metropolis-Hastings-within-Gibbs algorithm. Strategies for combining component-wise updates include composition, random sequence and random scans. While these strategies can ease MCMC implementation and produce superior empirical performance compared to full-dimensional updates, the theoretical convergence properties of the associated Markov chains have received limited attention. We present conditions under which some component-wise Markov chains converge to the stationary distribution at a geometric rate. We pay particular attention to the connections between the convergence rates of the various component-wise strategies. This is important since it ensures the existence of tools that an MCMC practitioner can use to be as confident in the simulation results as if they were based on independent and identically distributed samples. We illustrate our results in two examples including a hierarchical linear mixed model and one involving maximum likelihood estimation for mixed models.

Published in at http://dx.doi.org/10.1214/13-STS423 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (9)

• Markov Chain Monte Carlo: Can We Trust the Third Significant Figure?
• Batch means and spectral variance estimators in Markov chain Monte Carlo
• Optimal scaling for partially updating MCMC algorithms
• Component-Wise Markov Chain Monte Carlo: Uniform and Geometric Ergodicity under Mixing and Composition
• Adaptive Gibbs samplers and related MCMC methods
• Variable transformation to obtain geometric ergodicity in the random-walk Metropolis algorithm
• Convergence analysis of the Gibbs sampler for Bayesian general linear mixed models with improper priors
• Kernel estimators of asymptotic variance for adaptive Markov chain Monte Carlo
• Gibbs Sampling, Exponential Families and Orthogonal Polynomials

Cited by in corpus (5)

• Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods
• Component-Wise Markov Chain Monte Carlo: Uniform and Geometric Ergodicity under Mixing and Composition
• The Recycling Gibbs Sampler for Efficient Learning
• Metropolis Sampling
• BAMCAFE: A Bayesian Machine Learning Advanced Forecast Ensemble Method for Complex Turbulent Systems with Partial Observations