STA 425 - COMPUTATIONAL BAYESIAN STATISTICS

College of Arts and Sciences

Credit(s): 3

Bayesian methods naturally provide powerful and flexible ways to account for uncertainty in real world data problems. This course introduces important topics in Bayesian analysis and modeling, with an emphasis on building useful models in the context of real data. Emphasis on model checking and modern tools for Bayesian computation, with an introduction to machine learning methods with Bayesian foundations.

Prereq: STA 305 and STA 315.
Approved for Distance Learning.