Bernoulli

  • Bernoulli
  • Volume 18, Number 1 (2012), 252-278.

On adaptive resampling strategies for sequential Monte Carlo methods

Pierre Del Moral, Arnaud Doucet, and Ajay Jasra

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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.

Article information

Source
Bernoulli, Volume 18, Number 1 (2012), 252-278.

Dates
First available in Project Euclid: 20 January 2012

Permanent link to this document
https://projecteuclid.org/euclid.bj/1327068625

Digital Object Identifier
doi:10.3150/10-BEJ335

Mathematical Reviews number (MathSciNet)
MR2888706

Zentralblatt MATH identifier
1236.60072

Keywords
random resampling sequential Monte Carlo methods

Citation

Del Moral, Pierre; Doucet, Arnaud; Jasra, Ajay. On adaptive resampling strategies for sequential Monte Carlo methods. Bernoulli 18 (2012), no. 1, 252--278. doi:10.3150/10-BEJ335. https://projecteuclid.org/euclid.bj/1327068625


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References

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