STAT 6450 - Applied Bayesian Statistics

Bayesian statistical techniques play a pivotal role in applied research today. Topics include various loss functions and optimal estimators, Bayes factor, hierarchical models, Markov Chain Monte Carlo simulation, robust Bayesian analysis, and non-parametric and semi-parametric Bayesian methods. Several interesting applications will be discussed from climate and weather sciences, medical and biological sciences, and machine learning and pattern recognition. For computation WINBUGS, R, and Matlab softwares will be used.

Prerequisites/Corequisites: Prerequisite: STAT 6600 with a grade of “B” or better, or instructor approval.

Credits: 3 hours

Notes: Open to Graduate students only.
When Offered: Every other Fall.