Intermediate Bayesian Statistical Modeling

HOURS: 10:00 - 11:45; Tu. and Th.

ROOM: BE 156

 This is an intermediate level statistics course covering the theory and the 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 (C, C++, F77, F95, Matlab, R, 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, model comparison.

Instructors and Assistants