Bayesian Data Analysis

Many fields of science are transitioning from null hypothesis significance testing (NHST) to Bayesian data analysis. Bayesian analysis provides rich information about the relative credibilities of all candidate parameter values for any descriptive model of the data, without reference to p values. Bayesian analysis applies flexibly and seamlessly to complex hierarchical models and realistic data structures, including small samples, large samples, unbalanced designs, missing data, censored data, outliers, etc. Bayesian analysis software is flexible and can be used for a wide variety of data-analytic models. This course shows you how to do Bayesian data analysis, hands on, with free software called R and JAGS. The course will use new programs and examples.

This course is closely modeled on the very successful series of workshops given by Prof. John Kruschke. We will be using his software, and I strongly recommend his book (see below) and his blog,

Course Objectives: You will learn

Course Audience
The intended audience is PhD students, faculty, and other researchers, from all disciplines, who want a ground-floor introduction to doing Bayesian data analysis.

Course Prerequisites
No specific mathematical expertise is presumed. In particular, no matrix algebra is used in the course. Some previous familiarity with statistical methods such as a t-test or linear regression can be helpful, as is some previous experience with programming in any computer language, but these are not critical.

Course Topics (Exact content, ordering, and durations may change.)

Day 1:

Day 2:

Day 3:

Day 4:

Day 5:

Highly recommended textbook

Doing Bayesian Data Analysis, 2nd Edition: A Tutorial with R, JAGS, and Stan. The book is a genuinely accessible, tutorial introduction to doing Bayesian data analysis. The software used in the course accompanies the book, and many topics in the course are based on the book. (The course uses the 2nd edition, not the 1st edition.) Further information about the book can be found at

Install software before arriving

It is important to bring a notebook computer to the course, so you can run the programs and see how their output corresponds with the presentation material.  Please install the software before arriving at the course. The software and programs are occasionally updated, so please check here a week before the course to be sure you have the most recent versions.

For complete installation instructions, please go to


Examination paper written at home (individual) 100%

Examination content

For students taking the course for credit, there are daily homework exercises, all due one week after the last day of class.

Students are encouraged to use whatever resources they can to successfully execute the homework exercises, but the ultimate execution and write‑up must be by their own hand and in their own words. An honor statement must accompany each submitted assignment.

Each day’s homework exercises will be computer‑based replications and extensions of examples in that day’s lecture. The exercises ask the student to reproduce the example, explain its meaning, and perform a specific extension or novel application.

Examination relevant literature

There is no additional literature needed for the homework exercises.