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On-Line Inference for Hidden Markov Models via Particle Filters.

Fearnhead, Paul; and Clifford, Peter (2003) On-Line Inference for Hidden Markov Models via Particle Filters. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 65 (4). pp. 887-899. ISSN 1369-7412

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Abstract

We consider the on-line Bayesian analysis of data by using a hidden Markov model, where inference is tractable conditional on the history of the state of the hidden component. A new particle filter algorithm is introduced and shown to produce promising results when analysing data of this type. The algorithm is similar to the mixture Kalman filter but uses a different resampling algorithm. We prove that this resampling algorithm is computationally efficient and optimal, among unbiased resampling algorithms, in terms of minimizing a squared error loss function. In a practical example, that of estimating break points from well-log data, our new particle filter outperforms two other particle filters, one of which is the mixture Kalman filter, by between one and two orders of magnitude.

Item Type: Article
Journal or Publication Title: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 9818
Deposited By: Mrs Yaling Zhang
Deposited On: 20 Jun 2008 11:13
Refereed?: Yes
Published?: Published
Last Modified: 28 Oct 2014 11:06
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/9818

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