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Joint segmentation of piecewise constant autoregressive processes by using a hierarchical model and a Bayesian sampling approach

Nicolas Dobigeon 1, 2 Jean-Yves Tourneret 1, 2 Manuel Davy 3, 4
1 IRIT-SC - Signal et Communications
IRIT - Institut de recherche en informatique de Toulouse
3 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
4 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : We propose a joint segmentation algorithm for piecewise constant AR processes recorded by several independent sensors. The algorithm is based on a hierarchical Bayesian model. Appropriate priors allow to introduce correlations between the change locations of the observed signals. Numerical problems inherent to Bayesian inference are solved by a Gibbs sampling strategy. The proposed joint segmentation methodology provides interesting results compared to a signal-by-signal segmentation.
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Submitted on : Tuesday, December 12, 2006 - 4:41:33 PM
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Nicolas Dobigeon, Jean-Yves Tourneret, Manuel Davy. Joint segmentation of piecewise constant autoregressive processes by using a hierarchical model and a Bayesian sampling approach. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2006), May 2006, Toulouse, France. ⟨10.1109/ICASSP.2006.1660575⟩. ⟨inria-00119997⟩

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