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MCMC, sufficient statistics and particle filters.

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MCMC, sufficient statistics and particle filters. / Fearnhead, Paul.
In: Journal of Computational and Graphical Statistics, Vol. 11, No. 4, 01.12.2002, p. 848-862.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Fearnhead, P 2002, 'MCMC, sufficient statistics and particle filters.', Journal of Computational and Graphical Statistics, vol. 11, no. 4, pp. 848-862. <http://www.ingentaconnect.com/content/asa/jcgs/2002/00000011/00000004/art00007>

APA

Vancouver

Fearnhead P. MCMC, sufficient statistics and particle filters. Journal of Computational and Graphical Statistics. 2002 Dec 1;11(4):848-862.

Author

Fearnhead, Paul. / MCMC, sufficient statistics and particle filters. In: Journal of Computational and Graphical Statistics. 2002 ; Vol. 11, No. 4. pp. 848-862.

Bibtex

@article{60a6ddd50119463e88b0577216d96b3c,
title = "MCMC, sufficient statistics and particle filters.",
abstract = "This article considers how to implement Markov chain Monte Carlo (MCMC) moves within a particle filter. Previous, similar, attempts have required the complete history ({"}trajectory{"}) of each particle to be stored. Here, it is shown how certain MCMC moves can be introduced within a particle filter when only summaries of each particles' trajectory are stored. These summaries are based on sufficient statistics. Using this idea, the storage requirement of the particle filter can be substantially reduced, and MCMC moves can be implemented more efficiently. We illustrate how this idea can be used for both the bearingsonly tracking problem and a model of stochastic volatility. We give a detailed comparison of the performance of different particle filters for the bearings-only tracking problem. MCMC, combined with a sensible initialization of the filter and stratified resampling, produces substantial gains in the efficiency of the particle filter.",
keywords = "BEARINGS-ONLY TRACKING, IMPORTANCE SAMPLING, STOCHASTIC VOLATILITY",
author = "Paul Fearnhead",
year = "2002",
month = dec,
day = "1",
language = "English",
volume = "11",
pages = "848--862",
journal = "Journal of Computational and Graphical Statistics",
issn = "1061-8600",
publisher = "American Statistical Association",
number = "4",

}

RIS

TY - JOUR

T1 - MCMC, sufficient statistics and particle filters.

AU - Fearnhead, Paul

PY - 2002/12/1

Y1 - 2002/12/1

N2 - This article considers how to implement Markov chain Monte Carlo (MCMC) moves within a particle filter. Previous, similar, attempts have required the complete history ("trajectory") of each particle to be stored. Here, it is shown how certain MCMC moves can be introduced within a particle filter when only summaries of each particles' trajectory are stored. These summaries are based on sufficient statistics. Using this idea, the storage requirement of the particle filter can be substantially reduced, and MCMC moves can be implemented more efficiently. We illustrate how this idea can be used for both the bearingsonly tracking problem and a model of stochastic volatility. We give a detailed comparison of the performance of different particle filters for the bearings-only tracking problem. MCMC, combined with a sensible initialization of the filter and stratified resampling, produces substantial gains in the efficiency of the particle filter.

AB - This article considers how to implement Markov chain Monte Carlo (MCMC) moves within a particle filter. Previous, similar, attempts have required the complete history ("trajectory") of each particle to be stored. Here, it is shown how certain MCMC moves can be introduced within a particle filter when only summaries of each particles' trajectory are stored. These summaries are based on sufficient statistics. Using this idea, the storage requirement of the particle filter can be substantially reduced, and MCMC moves can be implemented more efficiently. We illustrate how this idea can be used for both the bearingsonly tracking problem and a model of stochastic volatility. We give a detailed comparison of the performance of different particle filters for the bearings-only tracking problem. MCMC, combined with a sensible initialization of the filter and stratified resampling, produces substantial gains in the efficiency of the particle filter.

KW - BEARINGS-ONLY TRACKING

KW - IMPORTANCE SAMPLING

KW - STOCHASTIC VOLATILITY

M3 - Journal article

VL - 11

SP - 848

EP - 862

JO - Journal of Computational and Graphical Statistics

JF - Journal of Computational and Graphical Statistics

SN - 1061-8600

IS - 4

ER -