Intermediate Bayesian Statistical Modeling

This is a graduate statistics course covering the theory and methods used to build statistical models from a Bayesian perspective. It will be assumed that the student is familiar with the basic ideas of Bayesian methods, including computations using Monte Carlo. It will also be assumed that the student is familiar with a programming language (R, Matlab, Python, C, C++, F77, F95 or similar) at a level that allows the writing of relatively complex code to fit models with multiple parameters. Good familiarity with R is strongly recommended. Some of the topics that will be covered are: Hierarchical modeling, linear models (regression and analysis of variance), multivariate models, mixture models, predictive inference, and model comparison.

Class Time and Location: Tu-Th 8-9:45am in BE-156

Instructor Office Hours: Wed and Thu: 10-11am

TA Office Hours: Tue 2:30-3:30pm BE 312 C/D


Instructors and Assistants