Bayesian Methods
Basic Bayesian inference; conjugate and nonconjugate analyses; Markov Chain Monte Carlo methods; hierarchical modeling; convergence diagnostics.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesSTAT 536 & STAT 642
Course Outcomes: 

Apply, Implement and Interpret

Apply, implement and interpret a fully Bayesian approach to relevant statistical problems, including design, model selection, model fit steps

Generate Analysis

Generate their own analysis of Bayesian models in R

Understand, Explain, and Demonstrate

Understand, explain and demonstrate basic Baysian theory and its usefulness in real-world applications