Open Access
February 2012 On adaptive resampling strategies for sequential Monte Carlo methods
Pierre Del Moral, Arnaud Doucet, Ajay Jasra
Bernoulli 18(1): 252-278 (February 2012). DOI: 10.3150/10-BEJ335


Sequential Monte Carlo (SMC) methods are a class of techniques to sample approximately from any sequence of probability distributions using a combination of importance sampling and resampling steps. This paper is concerned with the convergence analysis of a class of SMC methods where the times at which resampling occurs are computed online using criteria such as the effective sample size. This is a popular approach amongst practitioners but there are very few convergence results available for these methods. By combining semigroup techniques with an original coupling argument, we obtain functional central limit theorems and uniform exponential concentration estimates for these algorithms.


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Pierre Del Moral. Arnaud Doucet. Ajay Jasra. "On adaptive resampling strategies for sequential Monte Carlo methods." Bernoulli 18 (1) 252 - 278, February 2012.


Published: February 2012
First available in Project Euclid: 20 January 2012

zbMATH: 1236.60072
MathSciNet: MR2888706
Digital Object Identifier: 10.3150/10-BEJ335

Keywords: random resampling , Sequential Monte Carlo methods

Rights: Copyright © 2012 Bernoulli Society for Mathematical Statistics and Probability

Vol.18 • No. 1 • February 2012
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