A Bayesian fusion approach to change-points analysis of processes

We present in this article a Bayesian estimation method for the fusion of change-points detection in a set of piecewise stationary processes. The estimate we propose is based on the maximization of the posterior distribution of the change instants conditionally to the process parameter estimation. It is defined as a penalized contrast function with a first term related to the fit to the observation and a second term of penalty. In the case of joint segmentation, the term of penalty is deduced from the prior law of the change instants. It is composed of parameters that guide the number and the position of changes and parameters that will bring prior information on the joint behavior of processes. We present the construction of the estimator for the fusion detection of changes in the mean and variance of the wind vector. The feasibility and the contribution of our method are shown on experimentations.