Convergence of posterior distribution in the mixture of regressions
Taeryon Choi · Journal of nonparametric statistics · 2008
Mixture models provide a method of modelling a complex probability distribution in terms of simpler structures. In particular, the method of mixture of regressions has received considerable attention due to its modelling flexibility and availability of convenient computational algorithms. This paper aims to contribute to theoretical justification for the mixtures of regression model from the Bayesian perspective. In particular, we establish consistency of posterior distribution and determine how fast posterior distribution converges to the true value of the parameter in the context of mixture of binary, Poisson, and Gaussian regressions.