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
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.
Recommended way to go through the material is
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.
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.
Great self study BDA3 exercises for this course are listed below. Most of these have also model solutions available.
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.