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.
Full text not available from this repository.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: | 26 Jul 2012 18:41 |
| Identification Number: | |
| URI: | http://eprints.lancs.ac.uk/id/eprint/9818 |
Actions (login required)
| View Item |
