Sequential Monte Carlo Samplers
Pierre Del Moral
CNRS-UMR C55830 and University Paul Sabatier, Toulouse, France.
Arnaud Doucet , Gareth W. Peters
Cambridge University, UK.
Summary. 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.
Keywords: Genetic Algorithm, Importance Sampling, Resampling, Markov chain Monte Carlo,
Sequential Monte Carlo, Simulated Annealing.
1. Introduction
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21
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6. Appendix
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Sequential Monte Carlo Samplers
References
23
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24
P. Del Moral, A. Doucet and G.W. Peters