Resampling from the past to improve on MCMC algorithms

Details Resampling from the past to improve on MCMC algorithms Resampling from the past to improve on MCMC algorithms

2006-05-16

arxiv.org/abs/math/0605452v1

Mathematics

Statistics Theory

26 pages, 7 figures

Scientific paper

We introduce the idea that resampling from past observations in a Markov Chain Monte Carlo sampler can fasten convergence. We prove that proper resampling from the past does not disturb the limit distribution of the algorithm. We illustrate the method with two examples. The first on a Bayesian analysis of stochastic volatility models and the other on Bayesian phylogeny reconstruction.

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