Markov Chain Monte Carlo and Rao{blackwellization
نویسندگان
چکیده
We introduce a form of Rao{Blackwellization for Markov chains which uses the transition distribution for conditioning. We show that for reversible Markov chains, this form of Rao{Blackwellization always reduces the asymptotic variance, and derive two explicit forms of the variance reduction obtained through repeated Rao{Blackwellization. The result applies to many Markov chain Monte Carlo methods used in practice. In particular, we discuss an application to data augmentation and give some simulation results for Ising model samplers.
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