Module 9: Bayesian data analysis

General description

This course introduces modern Bayesian statistics using the probabilistic programming language Stan and the front-end brms, used with R. We will cover the material in chapters 2-5 and 13 and 15 of this free online book (a pdf will be provided to participants):

Abbreviated lectures covering some important topics in chapter 1-4 can be previewed here for free:

Topics to be covered:

  • Bayes’ rule; conjugate analyses involving the Beta-Binomial and the Poisson-Gamma
  • Regression modeling
  • Hierarchical regression (aka linear mixed models)
  • Meta-analysis
  • Bayes factors

Participants are expected to prepare for the course by working through chapter 1 of the textbook before coming to class.

There will be five lectures spread out over five days; each lecture will be 90 minutes long, followed by 90 minutes of hands-on assignments (participants need to bring their own laptop with R, RStudio, Stan, and brms installed). Solutions to exercises will be provided during the course.

Target audience

Graduate students in linguistics, translation or interpreting studies.

Course prerequisites

Students are expected to have taken a course that covers:

  • Data annotation and simple descriptives of the data
  • Some important theoretical distributions (normal, binomial, uniform), and knowledge of probability density/mass functions, the cumulative distribution function, the inverse cumulative distribution function; multivariate distributions (roughly, the contents of chapter 1 of the textbook linked above)
  • Description of univariate and bivariate data (mean, variance, standard deviation, correlation, cross-tabulations)
  • Graphical presentation of univariate and bivariate data (bar chart, histogram, boxplot, qqplot, etc.)
  • Null and alternative hypothesis of t-test, simple linear regression, linear mixed modeling
  • Basic knowledge of R

Course materials

Copies of slides will be provided. The textbook is available here:

Teacher bio

Shravan Vasishth is professor of psycholinguistics and neurolinguistics at the University of Potsdam, Germany. Academic background: BA (Honours) in Japanese (JNU, India), Masters degrees in Linguistics (JNU, India),  Computer and Information Science (Ohio State, USA), and Statistics (Sheffield, UK); PhD in Linguistics (2002) from Ohio State. For more details, see


  • Monday 15/07/2024, 14:00-17:30
  • Tuesday 16/07/2024, 14:00-17:30
  • Wednesday 17/07/2024, 14:00-17:30
  • Thursday 18/07/2024, 14:00-17:30
  • Friday 19/07/2024, 14:00-17:30

In addition to these contact hours this module expects the following time for self-study:

A total of 10 hours time for self study during the course (including the in-class exercise time), and more time after the course is over to review the material.