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When using pyABC version >= 0.8, please cite:

  • Schälte, Y., Klinger, E., Alamoudi, E., Hasenauer, J., 2022. pyABC: Efficient and robust easy-to-use approximate Bayesian computation. Journal of Open Source Software. https://doi.org/10.21105/joss.04304.

    @article{schaelte2022pyabc,
  title = {pyABC: Efficient and robust easy-to-use approximate Bayesian computation},
  author = {Schälte, Yannik and Klinger, Emmanuel and Alamoudi, Emad and Hasenauer, Jan},
  journal = {Journal of Open Source Software},
  publisher = {The Open Journal},
  year = {2022},
  volume = {7},
  number = {74},
  pages = {4304},
  doi = {10.21105/joss.04304},
  url = {https://doi.org/10.21105/joss.04304},
}

When using pyABC version < 0.8 or functionality not introduced in later versions, please (also) cite:

  • Klinger, E., Rickert, D., Hasenauer, J., 2018. pyABC: distributed, likelihood-free inference. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty361.

    @article{klinger2018pyabc,
  title = {pyABC: distributed, likelihood-free inference},
  author = {Klinger, Emmanuel and Rickert, Dennis and Hasenauer, Jan},
  journal = {Bioinformatics},
  volume = {34},
  number = {20},
  pages = {3591--3593},
  year = {2018},
  publisher={Oxford University Press},
}

When presenting work that uses pyABC, feel free to use the icons in https://github.com/ICB-DCM/pyABC/tree/main/doc/logo, which are available under a CCO license.

pyABC has been cited and used in numerous publications, see e.g. Google Scholar.