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Issue ESAIM: Proc.
Volume 19, 2007
Conference Oxford sur les méthodes de Monte Carlo séquentielles
Page(s) 12 - 17
DOI http://dx.doi.org/10.1051/proc:071903
Published online 30 October 2007

ESAIM: Proc., 2007, Vol. 19, pp. 12-17
DOI: 10.1051/proc:071903

Particle filtering for continuous-time hidden Markov models

Nicolas Chopin1 and Elisa Varini2

1  ENSAE, France, and Bristol University, United Kingdom.
2  CNR, Milano, Italy.


(Published online: 30 October 2007)

Abstract
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.


Key words: Diffusion process, hidden Markov model, Jump Markov process, Particle filtering, Sequential Monte Carlo


© EDP Sciences, ESAIM 2007


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