Johannes Brachem

Statistics PhD student

Research
I work on the development of the Liesel framework for probabilistic programming, a Python framework for research on complex Bayesian modelling. I gratefully acknowledge the funding provided by the German Research Foundation (DFG) for the development of Liesel through grant 443179956.
In my PhD research, I work on Bayesian Conditional Transformation Regression Models, a very flexible class of distributional regression models. The goal of these models is to provide regression models that can capture all moments of the response's distribution and relate them to covariates - all without the assumption of a fixed parametric distribution.

Publications

• Brachem, J., Wiemann, P.F.V., Katzfuss, M. (2026) Data-Efficient Generative Modeling of Non-Gaussian Global Climate Fields via Scalable Composite Transformations. arXiv. https://doi.org/10.48550/arXiv.2602.23311
• Herp, M., Brachem, J., Altenbuchinger, M., Kneib, T. (2025) Graphical Transformation Models. arXiv. https://doi.org/10.48550/arXiv.2503.17845
• Brachem, J., Wiemann, P.F.V., Kneib, T. (2025) Bayesian Penalized Transformation Models: Structured Additive Location-Scale Regression for Arbitrary Conditional Distributions. arXiv. https://doi.org/10.48550/arXiv.2404.07440
• Brachem, J., Frank, M., Kvetnaya, T., Schramm, L. F. F., & Volz., L. (2022). Replikationskrise, p-hacking und Open Science – Eine Umfrage zu fragwürdigen Forschungspraktiken in studentischen Projekten und Impulse für die Lehre. Psychologische Rundschau, 73(1), 1-17. https://doi.org/10.1026/0033-3042/a000562
• Brachem, J., Heitzig, J., Keser, C. (2023) Election Risk in Climate Negotiations – a Lab Experiment with a Non-Convex Bargaining Set. Preprint. http://dx.doi.org/10.2139/ssrn.4624846
• Brachem, J., & Rothe, A. (2021). Stop removing stop words – An evaluation of preprocessing techniques for Twitter sentiment analysis with a deep learning approach. In R.-M. Kruse, B. Säfken, A. Silbersdorff, C. Weisser (Eds.), Learning deep textwork – Perspectives on natural language processing and artificial intelligence (pp. 37 – 53). Universitätsverlag Göttingen. https://doi.org/10.17875/gup2021-1608
• Brachem, J., Krüdewagen, H., Hagmayer, Y. (2019). The Limits of Nudging: Can Descriptive Social Norms Be Used to Reduce Meat Consumption? It's Probably Not That Easy. PsyArXiv. https://doi.org/10.31234/osf.io/xk58q.

Software

• Liesel – A Probabilistic Programming Framework [Python library] https://liesel-project.org
• Liesel-GAM – Generalized Additive Models in Liesel [Python library] https://github.com/liesel-devs/liesel_gam
• Liesel-PTM – Penalized Transformation Models in Liesel [Python library] https://github.com/liesel-devs/liesel-ptm
• PPPTM – Projected-Process Penalized Transformation Models in Liesel [Python library] https://github.com/jobrachem/ppptm
• alfred3 – A Library for Rapid Experiment Development [Python library] https://github.com/ctreffe/alfred
• alfred3-interact – Interactive web-experiments in alfred3 [Python library] https://github.com/jobrachem/alfred3-interact
• organizr – Tidy up your projects for good! [R package] https://github.com/jobrachem/organizr
• skewsamp – Sample size estimation for group comparisons with skewed data [R package] https://github.com/jobrachem/skewsamp

Teaching (current)

• M.WIWI-QMW.0001: Generalized Regression (Lecture) | Summer Term 2026
• M.WIWI-QMW.0037: Advanced Bayesian Inference (Exercise class) | Winter Term 2025/26

Teaching (past)

• M.WIWI-QMW.0037: Advanced Bayesian Inference (Exercise class) | Winter Term 2025/26
• M.WIWI-QMW.0037: Advanced Bayesian Inference (Exercise class) | Winter Term 2024/25
• M.WIWI-QMW.0037: Advanced Bayesian Inference (Exercise class) | Winter Term 2023/24
• B.WIWI-QMW.0001: Lineare Modelle (Exercise class) | Summer Term 2023
• M.WIWI-QMW.0002: Advanced Statistical Inference - Likelihood and Bayes (Exercise class) | Winter Term 2022/23
• B.WIWI-QMW.0001: Lineare Modelle (Exercise class) | Summer Term 2022