Dr. Xu teaches a variety of statistical courses at both graduate and undergraduate levels
Course Description The course will cover Bayesian methods for exploratory data analysis. The emphasis will be on applied data analysis in various disciplines. We will consider a variety of topics, including introduction to Bayesian inference, prior and posterior distribution, hierarchical models, spatial models, longitudinal models, models for categorical data and missing data, model checking and selection, computational methods by Markov Chain Monte Carlo using R. We will also cover some nonparametric Bayesian models if time allows, such as Gaussian processes.
Course Description This course covers advanced topics in Bayesian statistical analysis beyond the introductory course. There- fore knowledge of basic Bayesian statistics is assumed (at the level of “A first course in Bayesian statistical methods”, by Peter Hoff (Springer, 2009)). The models and computational methods will be introduced with emphasis on applications to real data problems. This course will cover nonparametric Bayesian models including Gaussian process, Dirichlet process (DP), Polya trees, dependent DP, Indian buffet process, etc.
EN.553.833: Bayesian Modeling in Biomedical Applications. Spring 2018
EN.553.831: Advanced Topics in Nonparametric Bayesian Statistics. Fall 2017
EN.553.732: Bayesian Statistics. Fall 2015, 2016, 2017, 2018