SYST 664 - Bayesian Inference and Decision Theory

Institution:

George Mason University

Subject:

Description:

Credits: 3 Cross-Listed with STAT 664 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. Prerequisites STAT 544 or 554, or equivalent. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered S

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.gmu.edu

Phone Number:

(703) 993-1000

Regional Accreditation:

Southern Association of Colleges and Schools

Calendar System:

Semester

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