2000 OnSequentialMonteCarloSamplingM

(Doucet et al., 2000) ⇒ Arnaud Doucet, Simon Godsill, and Christophe Andrieu. (2000). “On Sequential Monte Carlo Sampling Methods for Bayesian Filtering.” In: Statistics and Computing Journal, 10(3). doi:10.1023/A:1008935410038

Subject Headings: Sequential Monte Carlo Algorithm

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Author Keywords

Bayesian filtering; Rao-Blackwellised estimates; importance sampling; nonlinear non-Gaussian state space models; particle filtering; sequential Monte Carlo methods

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 deterministic 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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2000 OnSequentialMonteCarloSamplingM	Christophe Andrieu Arnaud Doucet Simon Godsill			On Sequential Monte Carlo Sampling Methods for Bayesian Filtering				10.1023/A:1008935410038