Bayesian sieve method for piece-wise smooth regression

We study the piece-wise smooth regression from a theoretical Bayesian perspective. Our results indicate that under some mild assumptions, the posterior of the regression model and the change-points locations contracts at optimal nonparametric convergence rate up to a log-factor, and the number of change-points is posterior consistent.