On Sequential Monte Carlo Sampling Methods for Bayesian Filtering

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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

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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