Bayesian change point detection for functional data

We propose a Bayesian method to detect change points in sequence of functional observations that are signal functions observed with noises. Since have unlimited features, it is natural think the driving underlying through an evolution process, is, different features over time but possibly at times. A change-point then viewed as cumulative effect changes many so dissimilarities before and after change-points maximum level. In our setting, characterized by wavelet coefficients their expansion. consider approach putting priors independently on functions, allowing values time. Then we compute posterior distribution point for each coefficients, obtain measure overall similarity between two this sequence, which leads notion minimizing across relative similarities within segment. study performance proposed simulation apply dataset climate change.