Liesel: A Probabilistic Programming Framework#

Welcome to the API documentation of Liesel, a probabilistic programming framework with a focus on semi-parametric regression. It includes:

  • Liesel, a library to express statistical models as Probabilistic Graphical Models (PGMs). Through the PGM representation, the user can build and update models in a natural way.

  • Goose, a library to build custom MCMC algorithms with several parameter blocks and MCMC kernels such as the No U-Turn Sampler (NUTS), the Iteratively Weighted Least Squares (IWLS) sampler, or different Gibbs samplers. Goose also takes care of the MCMC bookkeeping and the chain post-processing.

  • RLiesel, an R interface for Liesel which assists the user with the configuration of semi-parametric regression models such as Generalized Additive Models for Location, Scale and Shape (GAMLSS) with different response distributions, spline-based smooth terms and shrinkage priors.

The name “Liesel” is an homage to the Gänseliesel fountain, landmark of Liesel’s birth city Göttingen.


For installation instructions, see the README in the main repository.

Further Reading#

For a scientific discussion of the software, see our paper on arXiv. If you are looking for code examples, the tutorial book might come in handy.


Liesel is being developed by Paul Wiemann and Hannes Riebl at the University of Göttingen with support from Thomas Kneib. Important contributions were made by Joel Beck, Alex Afanasev, Gianmarco Callegher and Johannes Brachem. We are grateful to the German Research Foundation (DFG) for funding the development through grant 443179956.

University of Göttingen Funded by DFG

API Reference#


Liesel modeling framework.


Goose MCMC framework.


Extra distributions for JAX-TFP.


Extra bijectors for JAX-TFP.


Experimental Liesel add-ons.