ST440 - Applied Bayesian
- Prerequisites: ST 422 and ST 430
- Term & Frequency: Spring
- Student Audience: Graduate students seeking to learn to use Bayesian methods
- Credit: 3 credits
- Recent Texts: Doing Bayesian Data Analysis, 2nd Edition. J. Kruschke
- Recent Instructors: Brian Reich, Alyson Wilson
- Background and Goals: The goal of this course is to introduce Bayesian data analysis methods
to students who do not have a theoretical background in statistics.
- Content: Introduction to Bayesian concepts of statistical inference; Bayesian learning; Markov chain Monte Carlo methods using existing software (SAS and OpenBUGS); linear and hierarchical models; model selection and diagnostics.
- Alternatives: None
- Subsequent Courses: None
S1 2017 Sections:
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