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Authors
Yannik Schälte (0000-0003-1293-820X), Emmanuel Klinger, Emad Alamoudi (0000-0002-9129-4635), Jan Hasenauer (0000-0002-4935-3312)
Citation
Schälte et al., (2022). pyABC: Efficient and robust easy-to-use approximate Bayesian computation. Journal of Open Source Software, 7(74), 4304, https://doi.org/10.21105/joss.04304
@article{Schälte2022,
doi = {10.21105/joss.04304},
url = {https://doi.org/10.21105/joss.04304},
year = {2022},
publisher = {The Open Journal},
volume = {7},
number = {74},
pages = {4304},
author = {Yannik Schälte and Emmanuel Klinger and Emad Alamoudi and Jan Hasenauer},
title = {pyABC: Efficient and robust easy-to-use approximate Bayesian computation},
journal = {Journal of Open Source Software}
}
Tags
approximate Bayesian computation ABC likelihood-free inference high-performance computing parallel sequential Monte Carlo
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This work is licensed under a Creative Commons Attribution 4.0 International License.
