Bayesian Density Regression

  title={Bayesian Density Regression},
  author={David B. Dunson and Natesh S. Pillai and Ju-Hyun Park},
This article considers Bayesian methods for density regression, allowing a random probability distribution to change flexibly with multiple predictors. The conditional response distribution is expressed as a nonparametric mixture of regression models, with the mixture distribution changing with predictors. A class of weighted mixture of Dirichlet process (WMDP) priors is proposed for the uncountable collection of mixture distributions. It is shown that this specification results in a… CONTINUE READING