Volume 19, 2007Conference Oxford sur les méthodes de Monte Carlo séquentielles
|Page(s)||12 - 17|
|Published online||30 October 2007|
Particle filtering for continuous-time hidden Markov models
ENSAE, France, and Bristol University, United Kingdom.
2 CNR, Milano, Italy.
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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