Adaptive Gibbs samplers and related MCMC methods
Computation
2013-02-28 v2 Probability
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.
Keywords
Cite
@article{arxiv.1101.5838,
title = {Adaptive Gibbs samplers and related MCMC methods},
author = {Krzysztof Łatuszyński and Gareth O. Roberts and Jeffrey S. Rosenthal},
journal= {arXiv preprint arXiv:1101.5838},
year = {2013}
}
Comments
Published in at http://dx.doi.org/10.1214/11-AAP806 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org). arXiv admin note: substantial text overlap with arXiv:1001.2797