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.
https://hal.inria.fr/inria-00119997
Contributor : Manuel Loth
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Submitted on : Tuesday, December 12, 2006 - 4:41:33 PM
Last modification on : Thursday, June 27, 2019 - 4:27:42 PM
Long-term archiving on : Wednesday, April 7, 2010 - 12:28:47 AM
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 ICASSP 2006, 2006, Toulouse, France. ⟨inria-00119997⟩
