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Inférence bayésienne pour la détermination et la
sélection de modèles stochastiques

Abstract : We are interested in the addition of uncertainty in hidden Markov models. The inference is made in a Bayesian framework based on Monte Carlo methods. We consider multiple sensors that may switch between several states of work. An original jump model is developed for different kind of situations, including synchronous/asynchronous data and the binary valid/invalid case. The model/algorithm is applied to the positioning of a land vehicle equipped with three sensors. One of them is a GPS receiver, whose data are potentially corrupted due to multipaths phenomena.
We consider the estimation of the probability density function of the evolution and observation noises in hidden Markov models. First, the case of linear models is addressed and MCMC and particle filter algorithms are developed and applied on three different applications. Then the case of the estimation of probability density functions in nonlinear models is addressed. For that purpose, time-varying Dirichlet processes are defined for the online estimation of time-varying probability density functions.
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Contributor : Francois Caron <>
Submitted on : Wednesday, April 4, 2007 - 7:29:33 PM
Last modification on : Tuesday, November 24, 2020 - 2:18:22 PM
Long-term archiving on: : Friday, September 21, 2012 - 1:50:15 PM


  • HAL Id : tel-00140088, version 1



Francois Caron. Inférence bayésienne pour la détermination et la
sélection de modèles stochastiques. Traitement du signal et de l'image [eess.SP]. Ecole Centrale de Lille; Université des Sciences et Technologie de Lille - Lille I, 2006. Français. ⟨tel-00140088⟩



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