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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.<\/jats:p>","DOI":"10.1111\/1467-9868.00421","type":"journal-article","created":{"date-parts":[[2004,12,28]],"date-time":"2004-12-28T19:39:11Z","timestamp":1104262751000},"page":"887-899","source":"Crossref","is-referenced-by-count":148,"title":["On-Line Inference for Hidden Markov Models via Particle Filters"],"prefix":"10.1093","volume":"65","author":[{"given":"Paul","family":"Fearnhead","sequence":"first","affiliation":[{"name":"Lancaster University , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Clifford","sequence":"additional","affiliation":[{"name":"University of Oxford , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2003,10,28]]},"reference":[{"key":"2023032921502453200_","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/0005-1098(77)90028-0","article-title":"Random sampling approach to state estimation in switching environments","volume":"13","author":"Akashi","year":"1977","journal-title":"Automatica"},{"key":"2023032921502453200_","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0025-5564(92)90023-P","article-title":"Stochastic models for ion channels: introduction and bibliography","volume":"112","author":"Ball","year":"1992","journal-title":"Math. 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