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.