Johannes Brachem

Statistics PhD student

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.



  • 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.
  • 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.


  • 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