A Mixture-Based Approach to Regional Adaptation for MCMC

Details A Mixture-Based Approach to Regional Adaptation for MCMC A Mixture-Based Approach to Regional Adaptation for MCMC

2009-03-30

arxiv.org/abs/0903.5292v2

Journal of Computational and Graphical Statistics, 2011

Statistics

Computation

Scientific paper

10.1198/jcgs.2010.09035

Recent advances in adaptive Markov chain Monte Carlo (AMCMC) include the need for regional adaptation in situations when the optimal transition kernel is different across different regions of the sample space. Motivated by these findings, we propose a mixture-based approach to determine the partition needed for regional AMCMC. The mixture model is fitted using an online EM algorithm (see Andrieu and Moulines, 2006) which allows us to bypass simultaneously the heavy computational load and to implement the regional adaptive algorithm with online recursion (RAPTOR). The method is tried on simulated as well as real data examples.

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