UBC - University of British Columbia (Vancouver Campus, , 2329 West Mall, Vancouver, BC Canada V6T 1Z4 -
Okanagan Campus, 3333 University Way, Kelowna, BC Canada V1V 1V7 - Canada)
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.
https://hal.inria.fr/inria-00119993
Contributor : Manuel Loth
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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
Long-term archiving on : Thursday, September 20, 2012 - 3:51:16 PM
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⟩
