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On sequential Monte Carlo sampling methods for Bayesian filtering

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 sh...

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Publication status:
Published

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Publisher copy:
10.1023/A:1008935410038

Authors


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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Godsill, S More by this author
Andrieu, C More by this author
Journal:
STATISTICS AND COMPUTING
Volume:
10
Issue:
3
Pages:
197-208
Publication date:
2000-07-05
DOI:
ISSN:
0960-3174
URN:
uuid:2f2825fc-9e6f-4888-a7ab-4e65a4be97a2
Source identifiers:
190630
Local pid:
pubs:190630

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