Revisiting Bayesian curve fitting using multivariate normal mixtures ∗
Stephen Graham Walker, George Karabatsos · Oxford University Press eBooks · 2013
This chapter develops a Bayesian nonparametric regression model which relies on a standard Bayesian nonparametric form for the joint distribution of both the dependent and independent variables. The regression model then is available as a conditional density which can only be written as a ratio of two infinite-dimensional mixture models. The chapter is organized as follows. Section 15.2 describes the regression model and the methods for sampling the posterior distribution of the model. To obtain full posterior inference of the model, a reversible-jump sampling algorithm is used to deal with the uncomputable normalizing constant. Section 15.3 illustrates the model using data analysis.