This is the web page for the Bayesian Data Analysis course at Aalto (CS-E5710) by Aki Vehtari.

Note: The Webpage for the BDA GSU 2023 Course is here

Aalto students should check also MyCourses. In 2022 Aalto course can be taken online except for the final project presentation. The lectures will be given on campus, but recorded and the recording will be made available online after the course. The registration for the course lectures will be used to estimate the need for the resources. If you are unable to register for the course at the moment in the Sisu, there is no need to email the lecturer. You can start taking the course and register before the end of the course. Sisu shows rooms on campus for the computer exercises, but only some of the computer exercises and TA sessions are on campus and most of the session are online (we are preparing the schedule). You can choose which TA session to join each week separately, without a need to register for those sessions.

All the course material is available in a git repo (and these pages are for easier navigation). All the material can be used in other courses. Text (except the BDA3 book) and videos licensed under CC-BY-NC 4.0. Code licensed under BSD-3.

The electronic version of the course book Bayesian Data Analysis, 3rd ed, by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin is available for non-commercial purposes. Hard copies are available from the publisher and many book stores. See also home page for the book, errata for the book, and chapter notes.


This course has been designed so that there is strong emphasis in computational aspects of Bayesian data analysis and using the latest computational tools.

If you find BDA3 too difficult to start with, I recommend

Course contents following BDA3

Bayesian Data Analysis, 3rd ed, by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. Home page for the book. Errata for the book. Electronic edition for non-commercial purposes only.

How to study

Recommended way to go through the material is

  • Read the reading instructions for a chapter in chapter notes.
  • Read the chapter in BDA3 and check that you find the terms listed in the reading instructions.
  • Watch the corresponding lecture video to get explanations for most important parts.
  • Read corresponding additional information in the chapter notes.
  • Run the corresponding demos in R demos or Python demos.
  • Read the exercise instructions and make the corresponding assignments. Demo codes in R demos and Python demos have a lot of useful examples for handling data and plotting figures. If you have problems, visit TA sessions or ask in course slack channel.
  • If you want to learn more, make also self study exercises listed below

Slides and chapter notes

  • Slides
    • including code for reproducing some of the figures
  • Chapter notes
    • including reading instructions highlighting most important parts and terms


The following video motivates why computational probabilistic methods and probabilistic programming are important part of modern Bayesian data analysis.

Short video clips on selected introductory topics are available in a Panopto folder and listed below.

The 2022 lecture videos are in a Panopto folder.

R and Python

We strongly recommend using R in the course as there are more packages for Stan and statistical analysis in R. If you are already fluent in Python, but not in R, then using Python may be easier, but it can still be more useful to learn also R. Unless you are already experienced and have figured out your preferred way to work with R, we recommend installing RStudio Desktop or using Aalto teaching JupyterHub. See FAQ for frequently asked questions about R problems in this course. The demo codes provide useful starting points for all the assignments.


These demos include a lot of useful code for making the assignments.

Self study exercises

Great self study BDA3 exercises for this course are listed below. Most of these have also model solutions available.

  • 1.1-1.4, 1.6-1.8 (model solutions for 1.1-1.6)
  • 2.1-2.5, 2.8, 2.9, 2.14, 2.17, 2.22 (model solutions for 2.1-2.5, 2.7-2.13, 2.16, 2.17, 2.20, and 2.14 is in slides)
  • 3.2, 3.3, 3.9 (model solutions for 3.1-3.3, 3.5, 3.9, 3.10)
  • 4.2, 4.4, 4.6 (model solutions for 3.2-3.4, 3.6, 3.7, 3.9, 3.10)
  • 5.1, 5.2 (model solutions for 5.3-5.5, 5.7-5.12)
  • 6.1 (model solutions for 6.1, 6.5-6.7)
  • 9.1
  • 10.1, 10.2 (model solution for 10.4)
  • 11.1 (model solution for 11.1)


The course material has been greatly improved by the previous and current course assistants (in alphabetical order): Michael Riis Andersen, Paul Bürkner, Akash Dakar, Alejandro Catalina, Kunal Ghosh, Joona Karjalainen, Juho Kokkala, Måns Magnusson, Janne Ojanen, Topi Paananen, Markus Paasiniemi, Juho Piironen, Jaakko Riihimäki, Eero Siivola, Tuomas Sivula, Teemu Säilynoja, Jarno Vanhatalo.

The web page has been made with rmarkdown’s site generator.