Bayesian Inference on Regression Model with an Unknown Change Point

In this work, we describe a Bayesian procedure for detection of change-point when we have an unknown change point in regression model. Bayesian approach with posterior inference for change points was provided to know the particular change point that is optimal while Gibbs sampler was used to estimate the parameters of the change point model. The simulation experiments show that all the posterior means are quite close to their true parameter values. The performance of this method is recommended for multiple change points.