|Class Schedule:||Sec 01: MW 9:30 AM–10:50 AM in Jones 226|
|Textbook(s):||Ghosal and van der Vaart, Fundamentals of Nonparametric Bayesian Inference|
|Description:||Bayesian nonparametric methods are increasingly important tools in machine learning and statistics. We will discuss nonparametric Bayesian approaches to mixture models, latent feature models, hierarchical models, network models and high-dimensional regression models. Topics that will be covered include Dirichlet process, Chinese restaurant process, Pitman-Yor process, Indian buffet process, Gaussian process, and their computational techniques via Gibbs sampling and variational inference. Frequentist evaluations of posterior distributions will also be discussed in nonparametric and high-dimensional settings.
Prerequisite(s): STAT 30200