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

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>11/2003
<mark>Journal</mark>Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Number of pages13
<mark>Original language</mark>English


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.