STAT 681 - Bayesian Statistics

Units: 2
Bayes Theorem, subjective probability, conjugate priors, non-informative priors, posterior estimation, credible intervals, prediction, sensitivity analysis, comparison to classical procedures. MCMC, Gibbs sampling, hierarchical Bayesian models. Use of statistical software. Report writing.

Prerequisites: STAT 630.
Possible Instructional Methods: On-ground.
Grading: ABC/NC grading only.
Student Learning Outcomes - Upon successful completion of this course students will be able to:

Demonstrate the use of Bayes rule to compute posterior probabilities.
Apply Bayesian hierarchical models to real data examples.
Write R scripts using various R packages to use Bayesian models.