Bayesian Nonparametric Inference -- Why and How

• Activity Number: 278
• Type: Topic Contributed
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Peter Mueller*+ and Riten Mitra
• Companies: University of Texas at Austin and University of Louisville
• Keywords: Dirichlet process ; Polya tree ; semiparametric models ; dependent Dirichlet

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.