SYST 664: Bayesian Inference and Decision Theory
Course Information from University Catalog
Introduces decision theory and relationship to Bayesian statistical inference. Teaches commonalities, differences between Bayesian and frequentist approaches to statistical inference, how to approach statistics problem from Bayesian perspective, and how to combine data with informed expert judgment in a sound way to derive useful and policy relevant conclusions. Teaches necessary theory to develop firm understanding of when and how to apply Bayesian and frequentist methods; and practical procedures for inference, hypothesis testing, and developing statistical models for phenomena. Teaches fundamentals of Bayesian theory of inference, including probability as a representation for degrees of belief, likelihood principle, use of Bayes Rule to revise beliefs based on evidence, conjugate prior distributions for common statistical models, and methods for approximating the posterior distribution. Introduces graphical models for constructing complex probability and decision models from modular components.
When Offered: Spring
Hours of Lecture or Seminar per week: 3
Equivalent to CSI 674; STAT 664 (2014-2015 Catalog).
1 Course Sections Scheduled for Spring 2018
SYST 664 - DL1: |
M; 4:30 pm - 7:10 pm