# Sequential Monte Carlo samplers

@article{Moral2002SequentialMC, title={Sequential Monte Carlo samplers}, author={Pierre Del Moral and A. Doucet}, journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, year={2002}, volume={68} }

Summary. We propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time by using sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make parallel Markov chain Monte Carlo algorithms interact to perform global…

## 1,586 Citations

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This paper uses sequential Monte Carlo samplers for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions to yield an extremely flexible and general algorithm for Bayesian model comparison that is suitable for use in applications where the acceptance rate in reversible jump Markov chain Monte Carlo is low.

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This article addresses how to optimally divide sampling effort between the samplers of the different distributions, and proposes a new Monte Carlo divergence error criterion based on Jensen–Shannon divergence.

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Borders for the complexity of sequential Monte Carlo approximations for a variety of target distributions including finite spaces, product measures, and log-concave distributions including Bayesian logistic regression are obtained.

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