Joint segmentation of piecewise constant autoregressive processes by using a hierarchical model and a Bayesian sampling approach
Résumé
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.
Domaines
Apprentissage [cs.LG]| Origine | Fichiers éditeurs autorisés sur une archive ouverte |
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