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On-line inference for hidden Markov models via particle filters

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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.
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Keywords: Changepoints; Ion channel; Kalman filter; Markov chain Monte Carlo methods; Particle filters; Smoothing; Well-log data

Document Type: Research Article

Publication date: November 1, 2003

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