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 1467-9868Full text not available from this repository.
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.
|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|
|Deposited By:||Mrs Yaling Zhang|
|Deposited On:||20 Jun 2008 11:13|
|Last Modified:||09 Oct 2013 15:43|
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