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.
To get started using sbi begin by reading the Documentation.
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.
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).
If you use sbi consider citing the sbi software paper, in addition to the original research articles describing the specific sbi-algorithm(s) you are using.
@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}
}The above citation refers to the original version of the sbi project and has a persistent DOI.
Additionally, new releases of sbi are citable via Zenodo, where we create a new DOI for every release.