Bayesian Data Analysis course

Resources

• Course website
• 2021 Schedule
• GitHub repo (my fork)
• Bayesian Data Analysis (3e) (BDA3) (exercise solutions)
• Chapter Notes
• Video lectures or individually lists here
• Lecture slides

How to study

The following are recommendations from the course creators on how to take the course.

The recommended way to go through the material is:

1. Read the reading instructions for a chapter in the chapter notes.
2. Read the chapter in BDA3 and check that you find the terms listed in the reading instructions.
3. Watch the corresponding video lecture to get explanations for most important parts.
4. Read corresponding additional information in the chapter notes.
5. Run the corresponding demos in R demos or Python demos.
6. 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.
7. If you want to learn more, make also self study exercises listed below.

Notes

My notes are published as a website here.