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arXiv · 1101.5838

Adaptive Gibbs samplers and related MCMC methods

Krzysztof Łatuszyński·Gareth O. Roberts·Jeffrey S. Rosenthal
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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.

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Krzysztof Łatuszyński, Gareth O. Roberts, Jeffrey S. Rosenthal. 2011-01-31. Adaptive Gibbs samplers and related MCMC methods. https://doi.org/10.1214/11-aap806

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