title: Bayesian Nonparametric Inference - Why and How.
abstract: We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametric inference. We discuss inference for density estimation, clustering, regression and for mixed effects models with random effects distributions. While we focus on arguing for the need for the flexibility of BNP models, we also review some of the more commonly used BNP models, thus hopefully answering a bit of both questions, why and how to use BNP.
authors.affiliation: Department of Mathematics, University of Texas, pmueller@math.utexas.edu.
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
authors.affiliation: ICES, University of Texas, riten82@gmail.com.
authors.forename: Riten
authors.initials: R
authors.lastname: Mitra
keywords: Dirichlet process; Nonparametric models; Polya tree; dependent Dirichlet process