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Bayesian Inference for Linear 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 Dirichlet process mixtures is introduced. Efficient Markov chain Monte Carlo and Sequential Monte Carlo methods are then developed to perform optimal batch and sequential estimation in such contexts. The algorithms are applied to blind deconvolution and change point detection. Experimental results on synthetic and real data demonstrate the efficiency of this approach in various contexts.
https://hal.inria.fr/inria-00129646
Contributor : Manuel Davy Connect in order to contact the contributor
Submitted on : Thursday, February 8, 2007 - 5:16:51 PM
Last modification on : Thursday, January 20, 2022 - 4:12:36 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 11:40:19 PM
Contributor : Manuel Davy Connect in order to contact the contributor
Submitted on : Thursday, February 8, 2007 - 5:16:51 PM
Last modification on : Thursday, January 20, 2022 - 4:12:36 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 11:40:19 PM
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IEEESP06R3.pdf
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