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Filtering and change point estimation for hidden Markov-modulated Poisson processes |
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| Affiliation: | 1. School of Mathematical Sciences, University of Adelaide, Adelaide, South Australia, Australia;2. Haskayne School of Business, University of Calgary, Calgary, Alberta, Canada;3. Cass Business School, City University London, 106 Bunhill Row, London, ECY1 8TZ, United Kingdom;4. Department of Applied Finance and Actuarial Studies, Faculty of Business and Economics, Macquarie University, Australia |
| Abstract: | A continuous-time Markov chain which is partially observed in Poisson noise is considered, where a structural change in the dynamics of the hidden process occurs at a random change point. Filtering and change point estimation of the model is discussed. Closed-form recursive estimates of the conditional distribution of the hidden process and the random change point are obtained, given the Poisson process observations |
| Keywords: | Continuous-time hidden Markov chain Poisson processes Reference probability approach Filtering Change-point estimation |
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