On adaptive resampling strategies for sequential Monte Carlo methods

Details On adaptive resampling strategies for sequential Monte Carlo methods On adaptive resampling strategies for sequential Monte Carlo methods

2012-03-02

arxiv.org/abs/1203.0464v1

Bernoulli 2012, Vol. 18, No. 1, 252-278

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.3150/10-BEJ335 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statisti

Scientific paper

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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