tag:joss.theoj.org,2005:/papers/tagged/sequential%20Monte%20CarloJournal of Open Source Software2022-06-25T17:36:18ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/34442022-06-25T17:36:18Z2022-06-26T00:01:16ZpyABC: Efficient and robust easy-to-use approximate Bayesian computationaccepted0.12.22022-03-26 22:08:05 UTC742022-06-25 17:36:18 UTC720224304YannikSchälteFaculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany, Institute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany, Center for Mathematics, Technical University Munich, Garching, Germany0000-0003-1293-820XEmmanuelKlingerInstitute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany, Center for Mathematics, Technical University Munich, Garching, Germany, Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, GermanyEmadAlamoudiFaculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany0000-0002-9129-4635JanHasenauerFaculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany, Institute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany, Center for Mathematics, Technical University Munich, Garching, Germany0000-0002-4935-331210.21105/joss.04304https://doi.org/10.5281/zenodo.6677826Python, Makohttps://joss.theoj.org/papers/10.21105/joss.04304.pdfapproximate Bayesian computation, ABC, likelihood-free inference, high-performance computing, parallel, sequential Monte Carlo