Change-point detection in astronomical data by using a hierarchical model and a bayesian sampling approach
Detection of significant intensity variations in astronomical time-series can be achieved with a hierarchical Bayesian approach to a piecewise constant Poisson rate model. A Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. Results with real and synthetic photon counting data illustrate the performance of the proposed algorithm. An extension to joint segmentation of multiple time series is also discussed