Implementation of reversible jump MCMC algorithm to segment the piecewise Polynomial Regression
Piecewise polynomial regression is very flexible model for modeling the data. If the piecewise polynomial regression is matched against the data, its parameters are not generally known. This paper studies a parameter estimation problem of the piecewise polynomial regression. The method which is used to estimate the parameters of the piecewise polynomial regression is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm produces the Markov chain that converges to the posterior distribution of piecewise polynomial regression parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of the piecewise polynomial regression.