Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Signal Processing Year : 2008

Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures

François Caron
  • Function : Author
  • PersonId : 838035
Manuel Davy
  • Function : Author
  • PersonId : 838036
Arnaud Doucet
  • Function : Author
  • PersonId : 846468
Emmanuel Duflos
  • Function : Author
  • PersonId : 838037
Philippe Vanheeghe
  • Function : Author
  • PersonId : 838038

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.
Fichier principal
Vignette du fichier
IEEESP06R3.pdf (387.05 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

inria-00129646 , version 1 (08-02-2007)

Identifiers

Cite

François Caron, Manuel Davy, Arnaud Doucet, Emmanuel Duflos, Philippe Vanheeghe. Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures. IEEE Transactions on Signal Processing, 2008, 56 (1), pp.71-84. ⟨10.1109/TSP.2007.900167⟩. ⟨inria-00129646⟩
346 View
284 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More