Adaptive Gibbs samplers and related MCMC methods

Details Adaptive Gibbs samplers and related MCMC methods Adaptive Gibbs samplers and related MCMC methods

2011-01-31

arxiv.org/abs/1101.5838v1

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substantial revision and extension of arXiv:1001.2797

Scientific paper

We consider various versions of adaptive Gibbs and Metropolis- within-Gibbs samplers, which update their selection probabilities (and per- haps also their proposal distributions) on the y during a run, by learning as they go in an attempt to optimise 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.

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