https://github.com/sib-swiss/intro-bayesian-statistics-training
SIB course on bayesian statistics with applications using Rstan
https://github.com/sib-swiss/intro-bayesian-statistics-training
bayesian-statistics r statistics training training-materials
Last synced: 7 months ago
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SIB course on bayesian statistics with applications using Rstan
- Host: GitHub
- URL: https://github.com/sib-swiss/intro-bayesian-statistics-training
- Owner: sib-swiss
- License: cc-by-4.0
- Created: 2022-01-20T08:41:05.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-01-28T08:20:34.000Z (8 months ago)
- Last Synced: 2025-01-28T08:28:14.247Z (8 months ago)
- Topics: bayesian-statistics, r, statistics, training, training-materials
- Language: HTML
- Homepage: https://sib-swiss.github.io/intro-bayesian-statistics-training/
- Size: 107 MB
- Stars: 26
- Watchers: 3
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
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README
[](https://doi.org/10.5281/zenodo.8070046)
# Introduction to Bayesian statistics with R
This course material is part of the "Introduction to Bayesian statistics with R" two-day course of [SIB Training](https://www.sib.swiss/training/upcoming-training-courses) 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](https://www.r-project.org/) programming language and the [Rstan](https://mc-stan.org/users/interfaces/rstan) and [brms](https://cran.r-project.org/web/packages/brms/index.html) 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.
## prerequisite installation
To follow this course, make sure you have [R](https://www.r-project.org/) and [Rstudio](https://www.rstudio.com/) installed beforehand.
Additionally, make sure to have the following R libraries installed:
* The [Rstan](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started) package (warning, there are 2 steps to the installation: Configuring C++ toolchains, and then installation of Rstan)
* [Rmarkdown](https://rmarkdown.rstudio.com/lesson-1.html)
* [Shiny](https://shiny.rstudio.com/tutorial/written-tutorial/lesson1/)
* [tidyverse](https://www.tidyverse.org/packages/)
* [BRMS](https://cran.r-project.org/web/packages/brms/index.html)
## course material organization
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.
## Citation
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
## Series of talks
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](https://youtu.be/5_Dx3x9L6UU) |
| Zoltan Kutalik (University of Lausanne and SIB) | Informative Bayesian priors boost power in genome-wide association studies | [Video](https://youtu.be/xQ46n5jbhyY) |
| Simone Tiberi (University of Bologna) | Bayesian approaches in computational biology | [Video](https://youtu.be/P_wXv1iFlSk) |
| Daniele Silvestro (University of Fribourg and SIB) | Bayesian neural networks | [Video](https://youtu.be/O0KPqrwshPw) |