Geodesic
Particle filtering for continuous-time hidden Markov models
Nicolas Chopin1 ; Elisa Varini2
1 ENSAE, France, and Bristol University, United Kingdom.
2 CNR, Milano, Italy.
ESAIM. Proceedings, Tome 19 (2007), pp. 12-17 Cet article a éte moissonné depuis la source EDP Sciences

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We consider continuous-time models where the observed process depends on an unobserved jump Markov Process. We develop a sequential Monte Carlo algorithm which makes it possible to filter and smooth this latent process, and compute the likelihood pointwise. We develop a Rao-Blackwellisation technique which allows to significantly reduce the Monte Carlo noise of this algorithm. Possible extensions of our algorithm and further directions of research are discussed.
DOI : 10.1051/proc:071903

Nicolas Chopin 1 ; Elisa Varini 2

1 ENSAE, France, and Bristol University, United Kingdom.
2 CNR, Milano, Italy. 
@article{EP_2007_19_a3,
     author = {Nicolas Chopin and Elisa Varini},
     title = {Particle filtering for continuous-time hidden {Markov} models},
     journal = {ESAIM. Proceedings},
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     year = {2007},
     volume = {19},
     doi = {10.1051/proc:071903},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1051/proc:071903/}
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Nicolas Chopin; Elisa Varini. Particle filtering for continuous-time hidden Markov models. ESAIM. Proceedings, Tome 19 (2007), pp. 12-17. doi: 10.1051/proc:071903

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