STAT 481 - Bayesian Statistics

Units: 3
Undergraduate course focusing on concepts, methodology and computation, with real data applications. Topics include Bayes’ theorem, prior, posterior and predictive distributions, and hierarchical models. Computational strategies such as MCMC, model diagnostics and selection will be discussed.

Prerequisites: One of: STAT 316, STAT 320, STAT 321, STAT 330.
Possible Instructional Methods: Entirely On-ground.
Grading: A-F grading only.
Student Learning Outcomes - Upon successful completion of this course students will be able to:

Derive and understand the Bayesian principles in estimation and hypothesis testing.
Formulate and model practical problems for solutions using these statistical methodologies.
Produce relevant computer output using standard statistical software and interpret results appropriately.
Communicate statistical concepts and analytical results clearly and appropriately to others.