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:
Journal Article
Journal or Publication Title:
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/qa
Subjects:
ID Code:
9818
Deposited By:
Users 810 not found.
Deposited On:
20 Jun 2008 10:13
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Jul 2020 10:52