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A uniformly convergent adaptive particle filter
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UNSPECIFIED (2005) A uniformly convergent adaptive particle filter. JOURNAL OF APPLIED PROBABILITY, 42 (4). pp. 1053-1068. ISSN 0021-9002.
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Abstract
Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number of particles go to infinity. The filter presented here generalizes Del Moral's Monte Carlo particle filter.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Journal or Publication Title: | JOURNAL OF APPLIED PROBABILITY | ||||
Publisher: | APPLIED PROBABILITY TRUST | ||||
ISSN: | 0021-9002 | ||||
Official Date: | December 2005 | ||||
Dates: |
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Volume: | 42 | ||||
Number: | 4 | ||||
Number of Pages: | 16 | ||||
Page Range: | pp. 1053-1068 | ||||
Publication Status: | Published |
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