Zero Variance Markov Chain Monte Carlo for Bayesian Estimators

Details Zero Variance Markov Chain Monte Carlo for Bayesian Estimators Zero Variance Markov Chain Monte Carlo for Bayesian Estimators

2010-12-14

arxiv.org/abs/1012.2983v1

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40 pages, 3 figures

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A general purpose variance reduction technique for Markov chain Monte Carlo estimators based on the zero-variance principle introduced in the physics literature by Assaraf and Caffarel (1999, 2003), is proposed. Conditions for unbiasedness of the zero-variance estimator are derived. A central limit theorem is also proved under regularity conditions. The potential of the new idea is illustrated with real applications to Bayesian inference for probit, logit and GARCH models.

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