Gießen, Germany · 17 + 18.09.2026
Everything you need for the workshop lives in this repository: JASP files, datasets, and guides. Nothing here requires a GitHub account — use the green Code → Download ZIP button if you would rather have the whole thing as a folder.
Please install the software before day 1 — as sometimes troubleshoot installation takes a while. If you have any questions, don't hesitate to send us a mail up front (bayescourse@gmail.com).
➡️ setup/README.md — step-by-step install guide for JASP, R, RStudio and the Stan toolchain, plus a script that checks whether everything works.
And, if you can: bring a dataset of your own — ideally one you have already analysed with frequentist methods. On day 2 you will re-analyse it the Bayesian way. See bring-your-own-data/.
Estimation, testing, and why any of it is worth your time. All hands-on work in JASP; no coding required.
| Session | Lead |
|---|---|
| Introduction & peer exchange | Julius |
| Bayesian basics — learning cycle, probability, Bayes' theorem | Julius |
| Bayesian estimation — theory + beta-binomial in JASP | Julius |
| Bayesian testing — theory + beta-binomial in JASP | Julius |
| Bayesian benefits — the benefits of going Bayesian | Julius |
| JASP with real data I — correlation | Henrik |
| JASP with real data II — A/B test & t-test | Henrik |
| JASP with real data III — two proportions | Henrik |
| JASP AI | Henrik |
| Conclusions — further reading, discussion, feedback, outlook to day 2 | Julius & Henrik |
Sessions run in this order, with breaks along the way.
📂 Materials: day-1/
From one model to many, then into R for mixed-effects models with brms.
| Session | Lead |
|---|---|
| Bayesian multi-model inference — theory, linear regression & ANOVA in JASP | Henrik |
Mixed-effects regression in R with brms — theory, prior & posterior predictive checks, implementation |
Julius & Henrik |
| How to conduct and report a Bayesian analysis | Julius |
| Bring your own data — redo your last frequentist analysis | Julius & Henrik |
Beyond the workshop — SBC; Stan, bridgesampling, blavaan, Bayesian state-space models, Bayesian Meta-Analysis |
Julius & Henrik |
| Conclusions | Julius & Henrik |
Sessions run in this order, with breaks along the way.
📂 Materials: day-2/
| Folder | What you'll find |
|---|---|
setup/ |
Install guide and an installation-check script — start here |
day-1/ |
Day 1 slides, JASP files and datasets |
day-2/ |
Day 2 slides, JASP files, R material, and datasets |
bring-your-own-data/ |
Example datasets |
Julius M. Pfadt — researcher in Eric-Jan
Wagenmakers' lab at the University of Amsterdam and former DFG Walter-Benjamin
fellow. Works on Bayesian statistical modeling, in particular psychometrics,
reliability estimation, and structural equation modeling; develops tools for
JASP and the R package Bayesrel; co-founder of JASP Services B.V.
🔗 juliuspfadt.com
Henrik R. Godmann — PhD candidate at the Psychological Methods Department of the University of Amsterdam. Works on robust Bayesian inference, interrupted and non-linear time series, state-space models, and statistical software for applied researchers; contributes to JASP; co-founder of JASP Services B.V. 🔗 hrgodmann.github.io · github.com/hrgodmann
| JASP | Free, open-source, point-and-click. Most of the workshop happens here. |
| R + RStudio | For the brms session on day 2. |
brms / Stan |
Hierarchical and mixed-effects models. |
Ask us! Or send us an email (bayescourse@gmail.com). If something in these materials is broken or unclear after the workshop, an email is still the best way to reach us.
Materials are released under CC BY 4.0 — reuse and adapt them for your own teaching, with attribution. Code is additionally available under the MIT License.
If you use these materials, please cite them as:
Pfadt, J. M., & Godmann, H. R. (2026). Introduction to Bayesian Inference: A Hands-on Workshop for Early-Career Researchers. Gießen. https://github.com/hrgodmann/BayesianStatisticsWorkshop