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Bayesian Inference for Dynamic Models with Dirichlet Process Mixtures

Francois Caron 1 Manuel Davy 2, 3 Arnaud Doucet 4 Emmanuel Duflos 3 Philippe Vanheeghe 3
1 LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
3 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
4 Dept of Statistics & Dept of Computer Science
Abstract : Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on mixture of Dirichlet processes is introduced. Efficient Markov chain Monte Carlo and Sequential Monte Carlo methods are then developed to perform optimal estimation in such contexts.
Keywords : Bayesian nonparametrics Dirichlet Process Mixture Monte Carlo Markov Chain RaoBlackwellisation Particle filter
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https://hal.inria.fr/inria-00119993
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Submitted on : Tuesday, December 12, 2006 - 4:26:12 PM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM
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  • HAL Id : inria-00119993, version 1

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Francois Caron, Manuel Davy, Arnaud Doucet, Emmanuel Duflos, Philippe Vanheeghe. Bayesian Inference for Dynamic Models with Dirichlet Process Mixtures. 9th IEEE International Conference on Information Fusion, 2006, Florence, Italy. ⟨inria-00119993⟩

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