Course Description
Pre-Requisite(s):
At least one semester of mathematical statistics (STAT 3120 or 5190) and one course in linear models (STAT 5120 or equivalent), or instructor permission
This introductory, graduate level course will provide an introduction Bayesian methods with emphasis on modeling and applications. The topics to be covered include methods for forming prior distributions such as conjugate and noninformative priors, derivation of posterior and predictive distributions and their moments, and development of Bayesian models including linear regression, generalized linear models and hierarchical models.
