Hannes Riebl

I recently defended my PhD thesis “Semi-Parametric Distributional Regression in Forestry and Ecology: Software, Models and Applications”. My research interests are statistical modeling and computational statistics, especially semi-parametric regression, spatial statistics, and Bayesian inference, as well as applications in biology, forestry, and ecology.

Also see my GitHub and ORCID.


  • Jonas Glatthorn, Scott Appleby, Niko Balkenhol, Peter Kriegel, Likulunga Emmanuel Likulunga, Jing-Zhong Lu, Dragan Matevski, Andrea Polle, Hannes Riebl, Carmen Alicia Rivera Pérez, Stefan Scheu, Alexander Seinsche, Peter Schall, Andreas Schuldt, Severin Wingender, and Christian Ammer. Species diversity of forest floor biota in non-native Douglas-fir stands is similar to that of native stands. Ecosphere, 2023. DOI: 10.1002/ecs2.4609. Forthcoming.
  • Hannes Riebl, Nadja Klein, and Thomas Kneib. Modelling intra-annual tree stem growth with a distributional regression approach for Gaussian process responses. Journal of the Royal Statistical Society, Series C: Applied Statistics, 2023. DOI: 10.1093/jrsssc/qlad015.
  • Hannes Riebl, Paul F.V. Wiemann, and Thomas Kneib. Liesel: A Probabilistic Programming Framework for Developing Semi-Parametric Regression Models and Custom Bayesian Inference Algorithms. arXiv, 2022. DOI: 10.48550/arXiv.2209.10975.
  • Ieva Bebre, Hannes Riebl, and Peter Annighöfer. Seedling Growth and Biomass Production under Different Light Availability Levels and Competition Types. Forests, 2021. DOI: 10.3390/f12101376.


  • Liesel. A probabilistic programming framework for Python with a focus on semi-parametric regression. Homepage. GitHub.
  • lmls. A didactic R package for Gaussian location-scale regression with support for Fisher scoring, MCMC sampling, and bootstrapping. Homepage. GitHub.


  • Introduction to Statistical Programming (Lectures and Exercises, Winter 2021, 2022)
  • Advanced Statistical Programming with R (Lectures and Seminar, Summer 2020, 2021, 2022)
  • Practical Statistical Training (Seminar, Summer 2021, 2022)
  • Advanced Mathematics: Optimization (Exercises, Winter 2018, 2019)
  • Statistical Modeling and Advanced Regression Analyses (Exercises, Winter 2018)