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sbi is licensed under the Affero General Public License version 3 (AGPLv3) and

Copyright (C) 2020 Álvaro Tejero-Cantero, Jakob H. Macke, Jan-Matthis Lückmann, Michael Deistler, Jan F. Bölts.

Copyright (C) 2020 Conor M. Durkan.


sbi has been supported by the German Federal Ministry of Education and Research (BMBF) through the project ADIMEM, FKZ 01IS18052 A-D). ADIMEM is a collaborative project between the groups of Jakob Macke (Uni Tübingen), Philipp Berens (Uni Tübingen), Philipp Hennig (Uni Tübingen) and Marcel Oberlaender (caesar Bonn) which aims to develop inference methods for mechanistic models.

Important dependencies and prior art

  • sbi is the successor to delfi, a Theano-based toolbox for sequential neural posterior estimation developed at mackelab. If you were using delfi, we strongly recommend to move your inference over to sbi. Please open issues if you find unexpected behaviour or missing features. We will consider these bugs and give them priority.

  • sbi as a PyTorch-based toolbox started as a fork of conormdurkan/lfi, by Conor M.Durkan.

  • sbi uses density estimators from bayesiains/nflows by Conor M.Durkan, George Papamakarios and Artur Bekasov. These are proxied through pyknos, a package focused on density estimation.

  • sbi uses PyTorch and tries to align with the interfaces (e.g. for probability distributions) adopted by PyTorch.

  • See for a list of publications describing the methods implemented in sbi.


If you use sbi consider citing the corresponding paper:

  doi = {10.21105/joss.02505},
  url = {},
  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}