About sbi

sbi is a PyTorch package for simulation-based inference. Simulation-based inference is the process of finding parameters of a simulator from observations.

sbi takes a Bayesian approach and returns a full posterior distribution over the parameters of the simulator, conditional on the observations. The package implements a variety of inference algorithms, including amortized and sequential methods. Amortized methods return a posterior that can be applied to many different observations without retraining; sequential methods focus the inference on one particular observation to be more simulation-efficient.

Get started

To get started using sbi begin by reading the Documentation.

Feedback and Contributions

We welcome any feedback on how sbi is working for your inference problems (see Discussions) and are happy to receive bug reports, pull requests and other feedback (see contribute). We wish to maintain a positive community, please read our Code of Conduct.

Support

sbi has been supported by the German Federal Ministry of Education and Research (BMBF) through project ADIMEM (FKZ 01IS18052 A-D), project SiMaLeSAM (FKZ 01IS21055A) and the Tübingen AI Center (FKZ 01IS18039A).

Citation

The sbi package has grown and improved significantly since its initial release, with contributions from a large and diverse community. To reflect these developments and the expanded functionality, we published an updated JOSS paper. We encourage you to cite this newer version as the primary reference:

@article{BoeltsDeistler_sbi_2025,
  doi = {10.21105/joss.07754},
  url = {https://doi.org/10.21105/joss.07754},
  year = {2025},
  publisher = {The Open Journal},
  volume = {10},
  number = {108},
  pages = {7754},
  author = {Jan Boelts and Michael Deistler and Manuel Gloeckler and Álvaro Tejero-Cantero and Jan-Matthis Lueckmann and Guy Moss and Peter Steinbach and Thomas Moreau and Fabio Muratore and Julia Linhart and Conor Durkan and Julius Vetter and Benjamin Kurt Miller and Maternus Herold and Abolfazl Ziaeemehr and Matthijs Pals and Theo Gruner and Sebastian Bischoff and Nastya Krouglova and Richard Gao and Janne K. Lappalainen and Bálint Mucsányi and Felix Pei and Auguste Schulz and Zinovia Stefanidi and Pedro Rodrigues and Cornelius Schröder and Faried Abu Zaid and Jonas Beck and Jaivardhan Kapoor and David S. Greenberg and Pedro J. Gonçalves and Jakob H. Macke},
  title = {sbi reloaded: a toolkit for simulation-based inference workflows},
  journal = {Journal of Open Source Software}
}

This updated paper, with its expanded author list, reflects the broader community contributions and the package's enhanced capabilities in releases 0.23.0 and later.

If you are using a version of sbi prior to 0.23.0, please cite the original sbi software paper:

@article{tejero-cantero2020sbi,
  doi = {10.21105/joss.02505},
  url = {https://doi.org/10.21105/joss.02505},
  year = {2020},
  publisher = {The Open Journal},
  volume = {5},
  number = {52},
  pages = {2505},
  author = {Alvaro Tejero-Cantero and Jan Boelts and Michael Deistler and Jan-Matthis Lueckmann and Conor Durkan and Pedro J. Gonçalves and David S. Greenberg and Jakob H. Macke},
  title = {sbi: A toolkit for simulation-based inference},
  journal = {Journal of Open Source Software}
}