Adaptive Gibbs samplers and related MCMC methods

Abstract

We consider various versions of adaptive Gibbs and Metropolis-within- Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the fly during a run by learning as they go in an attempt to optimize the algorithm. We present a cautionary example of how even a simple-seeming adaptive Gibbs sampler may fail to converge.We then present various positive results guaranteeing convergence of adaptive Gibbs samplers under certain conditions. © Institute of Mathematical Statistics, 2013.