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
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.