STA 687

Bayesian Data Analysis

The course covers all the fundamental concepts of Bayesian methods, and works from the simplest models up through hierarchical models applied to various types of data. The course uses examples from a variety of disciplines. Topics include: The basics of Bayesian inference for single and multiparameter models, regression, hierarchical models, model checking, approximation of a posterior distribution by iterative and non-iterative sampling methods, and missing data.

Units: 4.0