Liesel: A Probabilistic Programming Framework

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Liesel is 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.
  • Liesel-GAM, a modeling library built on 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.

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  • Released:
Newer release available (0.5.4)

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Credits

Author: Paul Wiemann, Hannes Riebl, Johannes Brachem, Gianmarco Callegher

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Data verified by PyPI on Sep 13, 2026
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MIT
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Python <3.15, >=3.13

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machine-learning statistics

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