Joint segmentation of multivariate Poissonian time series. Application to burst and transient source experiments

This paper addresses the problem of detecting significant intensity variations in multiple Poissonian time-series. This detection is achieved by using a constant Poisson rate model and a hierarchical Bayesian approach. An appropriate Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. An extended model that includes constraints on the segment lengths is also proposed. Simulation results performed on synthetic and real data illustrate the performance of the proposed algorithm.

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