P533 : Bayesian Data Analysis
P533 is a tutorial introduction to doing Bayesian data analysis. The course is intended to make advanced Bayesian methods genuinely accessible to real graduate students. Advanced undergrads are also welcome. The course covers all the fundamental concepts of Bayesian methods, and works from the simplest models up through hierarchical models applied to various types of data. More details about content are provided below in the daily Schedule of Topics. Students from all fields are welcome and encouraged to enroll (see figure at right). The course uses examples from a variety of disciplines.
Semester(s) Offered: Spring
Class time: Tuesday, Thursday
Instructor: John Kruschke - firstname.lastname@example.org
Other Contact(s): John Kruschke - email@example.com
Prerequisites: No specific pre-requisites.
Algebra Required?: No matrix algebra used.
Calculus Required?: Calculus is not needed for assignments; is used on rare occasions for concepts and explanations.
Substantive Orientation: Any.
Statistical Orientation: Bayesian, all models.
Books used: Kruschke, J. K. (2015). Doing Bayesian Data Analysis, 2nd Edition: A Tutorial with R, JAGS, and Stan. Academic Press. https://sites.google.com/site/doingbayesiandataanalysis/
Applied/Theoretical: Applied with thorough explanations.
Software Used: R
How the software is used: For data analysis. Students also modify programs to adapt to different applications.
Problem Sets: Weekly.
Data Analysis: As part of assignments.
Keywords: Bayesian, proportions, means, analysis of variance, regression, logistic, ordinal, probit, categorical