SIB course Introduction to Bayesian statistics with R

Welcome to the website of the SIB course “Introduction to Bayesian statistics with R”, which is addressed to beginners wanting to become familiar with the core concepts of Bayesian statistics through lectures and applied examples.

The course is divided in different lectures, each consisting in a deck of slides and a series of exercises. The slides are available as shiny presentation, or as rendered html files. The exercises are distributed as a pdf file, with an additionnal pdf containing the solutions.

note: you can grab the whole course material in one go by cloning or downloading the git repository

Learning outcomes

After this course, you will be able to:

  • Recognise the core components of a Bayesian model
  • List the main concepts of methods for Bayesian inference
  • Implement a simple Bayesian model in R
  • Interpret the results of a Bayesian model

Authors

  • Jack Kuipers ORCiD - D-BSSE - ETH Zürich
  • Wandrille Duchemin ORCiD - Swiss Institute of Bioinformatics & University of Basel

Citation

If you re-use or mention this course material, please cite:

Jack Kuipers, & Wandrille Duchemin. (2025, May 16). Introduction to Bayesian statistics with R. Zenodo. https://doi.org/10.5281/zenodo.15434109

Attribution

This course is an original creation of Jack Kuipers.