Abstract
In this paper, we propose a general methodology to sample sequentially from a sequence of probability distributions known up to a normalizing constant and defined on a common space. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time using Sequential Monte Carlo methods. This methodology allows us not only to derive simple algorithms to make parallel Markov chain Monte Carlo runs interact in a principled way, but also to obtain new methods for global optimization and sequential Bayesian estimation. We demonstrate the performance of these algorithms through simulation for various integration and global optimization tasks arising in the context of Bayesian inference.
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