Applied Bayesian Statistics

Applied Bayesian Statistics
Bayesian analogs of t-tests, regression, ANOVA, ANCOVA, logistic regression, and Poisson regression implemented using Nimble, Stan, JAGS and Proc MCMC.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesSTAT 251 & STAT 330; Concurrent enrollment in STAT 340.
 ProgramsContaining STAT 451
Course Outcomes: 

Fit and Interpret Bayesian Model

Students will be able to apply, implement and interpret a fully Bayesian approach to relevant statistical problems, including design, model selection, model fit steps.

Write code in R

Students will be able to generate their own analysis of Bayesian models in R.

Understand, Explain, and Demonstrate

Students will be able to understand, explain and demonstrate basic Bayesian theory and its usefulness in real-world applications.