SOME ASPECTS OF BAYESIAN NONPARAMETRIC INFERENCE UNDER DENSITY ESTIMATION, REGRESSION AND SURVIVAL MODELS

In this article, we review inference under models with nonparametric Bayesian priors in density estimation, regression and survival analysis. We discuss some important statistical inference related to Bayesian approach for this purpose. We take some selected examples which are challenging for standard parametric inference. We discuss density estimation, regression and survival models with random effect distributions and we focus flexibility of Bayesian nonparametric models. For this purpose, we review some approaches including Dirichlet process, polya trees, spline regression, Wavelet and Neural Network.