| Course: | STAT 41510 |
| Title: | Bayesian Nonparametrics |
| Instructor(s): | Chao Gao |
| Class Schedule: | Sec 01: MW 9:30 AM–10:50 AM in Jones 226 |
| Office Hours: | |
| 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 |