Please use this identifier to cite or link to this item: https://doi.org/10.3150/10-BEJ335
Title: On adaptive resampling strategies for sequential Monte Carlo methods
Authors: Del Moral, P.
Doucet, A.
Jasra, A. 
Keywords: Random resampling
Sequential Monte Carlo methods
Issue Date: Feb-2012
Citation: Del Moral, P., Doucet, A., Jasra, A. (2012-02). On adaptive resampling strategies for sequential Monte Carlo methods. Bernoulli 18 (1) : 252-278. ScholarBank@NUS Repository. https://doi.org/10.3150/10-BEJ335
Abstract: 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. © 2012 ISI/BS.
Source Title: Bernoulli
URI: http://scholarbank.nus.edu.sg/handle/10635/125058
ISSN: 13507265
DOI: 10.3150/10-BEJ335
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