This course material is part of the "Introduction to Bayesian statistics with R" two-day course of SIB Training and is addressed to beginners wanting to become familiar with the core concepts of Bayesian statistics through lectures and applied examples.
The practical exercises are implemented in the widely used R programming language and the Rstan and brms libraries. They will enable participants to use standard Bayesian statistical tools and interpret their results.
This course material presumes the participant is familiar with both R and (frequentist) statistical inference.
To follow this course, make sure you have R and Rstudio installed beforehand.
Additionally, make sure to have the following R libraries installed:
- The Rstan package (warning, there are 2 steps to the installation: Configuring C++ toolchains, and then installation of Rstan)
- Rmarkdown
- Shiny
- tidyverse
- BRMS
The course material is organized in 8 lectures, with corresponding exercises.
The lectures can be found in the lectures/ folder,
where the correspond to Rmarkdown files that should be opened with Rstudio and then rendered as presentation
- lecture 1 : T-test recap
- lecture 2 : P-values and confidence intervals
- lecture 3 : Monte Carlo methods
- lecture 4 : Bayesian first steps
- lecture 5 : Bayesian t-tests (STAN + BRMS)
- lecture 6 : Robust t-tests and priors
- lecture 7 : Bayesian linear regression
- lecture 8 : Bayesian logistic regression
Each lecture is accompanied by one or two exercises which can be found in the exercises/ folder, which contains the exercises instructions and solutions (as .pdf files), as well as the data files used in the exercise (in the data/) subfolder.
If you re-use or mention this course material, please cite:
Jack Kuipers, & Wandrille Duchemin. (2023, June 22). Introduction to Bayesian statistics with R. Zenodo. https://doi.org/10.5281/zenodo.8070046
In the previous iteration of this course (2023), experts in the field presented state-of-the-art Bayesian methods and their application in the life sciences. The recordings of their talks and slides can be found below:
| Speaker | Talk title | Links to |
|---|---|---|
| Timothy Vaughan (BSSE-ETHZ and SIB) | Bayesian foundations of Phylogenetic and Phylodynamic inference | Video |
| Zoltan Kutalik (University of Lausanne and SIB) | Informative Bayesian priors boost power in genome-wide association studies | Video |
| Simone Tiberi (University of Bologna) | Bayesian approaches in computational biology | Video |
| Daniele Silvestro (University of Fribourg and SIB) | Bayesian neural networks | Video |