sbi: A toolkit for simulation-based inference

Python Jupyter Notebook Submitted 14 July 2020Published 21 August 2020

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Alvaro Tejero-Cantero (0000-0002-8768-4227), Jan Boelts (0000-0003-4979-7092), Michael Deistler (0000-0002-3573-0404), Jan-Matthis Lueckmann (0000-0003-4320-4663), Conor Durkan (0000-0001-9333-7777), Pedro J. Gonçalves (0000-0002-6987-4836), David S. Greenberg (0000-0002-8515-0459), Jakob H. Macke (0000-0001-5154-8912)


Tejero-Cantero et al., (2020). sbi: A toolkit for simulation-based inference. Journal of Open Source Software, 5(52), 2505,

@article{Tejero-Cantero2020, 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} }
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simulation science likelihood-free inference bayesian inference system identification parameter identification

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