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Bayesian Inference for Dynamic Models with Dirichlet Process Mixtures
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.
Cited literature [13 references]
https://hal.inria.fr/inria-00119993
Contributor : Manuel Loth <>
Submitted on : Tuesday, December 12, 2006 - 4:26:12 PM
Last modification on : Friday, December 18, 2020 - 5:30:02 PM
Long-term archiving on: : Thursday, September 20, 2012 - 3:51:16 PM
Contributor : Manuel Loth <>
Submitted on : Tuesday, December 12, 2006 - 4:26:12 PM
Last modification on : Friday, December 18, 2020 - 5:30:02 PM
Long-term archiving on: : Thursday, September 20, 2012 - 3:51:16 PM
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Caron_Fusion_2006.pdf
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