Improving SMC sampler estimate by recycling all past simulated particles

Thi Le Thu Nguyen, François Septier, Gareth W. Peters, Yves Delignon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Sequential Monte Carlo (SMC) methods are not only a popular tool in the analysis of state-space models, but offer a powerful alternative to Markov chain Monte Carlo (MCMC) in situations where static Bayesian inference must be performed via simulation. In this paper, we propose a recycling scheme of all past simulated particles in the SMC sampler in order to reduce the variance of the final estimator. We demonstrate how the proposed approach outperforms the classical strategy in two challenging models.

Original languageEnglish
Title of host publication2014 IEEE Workshop on Statistical Signal Processing (SSP)
PublisherIEEE
Pages117-120
Number of pages4
ISBN (Print)9781479949755
DOIs
Publication statusPublished - 28 Aug 2014
Event17th IEEE Workshop on Statistical Signal Processing 2014 - Gold Coast, Australia
Duration: 29 Jun 20142 Jul 2014

Conference

Conference17th IEEE Workshop on Statistical Signal Processing 2014
Abbreviated titleSSP 2014
CountryAustralia
CityGold Coast
Period29/06/142/07/14

Keywords

  • Bayesian Inference
  • Recycling scheme
  • Sequential Monte Carlo sampler
  • Variance reduction

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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  • Cite this

    Nguyen, T. L. T., Septier, F., Peters, G. W., & Delignon, Y. (2014). Improving SMC sampler estimate by recycling all past simulated particles. In 2014 IEEE Workshop on Statistical Signal Processing (SSP) (pp. 117-120). IEEE. https://doi.org/10.1109/SSP.2014.6884589