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On Sequential Monte Carlo Sampling Methods for Bayesian Filtering (2000)

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by Arnaud Doucet , Simon Godsill , Christophe Andrieu
Venue:STATISTICS AND COMPUTING
Citations:1051 - 76 self
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BibTeX

@ARTICLE{Doucet00onsequential,
    author = {Arnaud Doucet and Simon Godsill and Christophe Andrieu},
    title = {On Sequential Monte Carlo Sampling Methods for Bayesian Filtering},
    journal = {STATISTICS AND COMPUTING},
    year = {2000},
    volume = {10},
    number = {3},
    pages = {197--208}
}

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Abstract

In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the determin-istic filtering literature; these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.

Keyphrases

bayesian filtering    sequential monte carlo sampling method    important class    particular interest    discrete time dynamic model    local linearisation method    effective importance distribution    general importance    analytic structure present    several different scientific discipline    state-space model    dynamic model    last decade    sequential simulation    final section    posterior distribution    novel extension    determin-istic filtering literature   

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