sbi: A toolkit for simulation-based inference
sbi: A toolkit for simulation-based inference - Published in JOSS (2020)
Science Score: 87.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
✓DOI references
Found 1 DOI reference(s) in JOSS metadata -
○Academic links in README
-
○Academic email domains
-
○Institutional organization owner
-
✓JOSS paper metadata
Published in Journal of Open Source Software
Last synced: 7 months ago
·
JSON representation
JOSS Publication
sbi: A toolkit for simulation-based inference
Published
August 21, 2020
Volume 5, Issue 52, Page 2505
Authors
Alvaro Tejero-Cantero
Equally contributing authors, Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich
Equally contributing authors, Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich
Jan Boelts
Equally contributing authors, Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich
Equally contributing authors, Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich
Michael Deistler
Equally contributing authors, Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich
Equally contributing authors, Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich
Jan-Matthis Lueckmann
Equally contributing authors, Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich
Equally contributing authors, Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich
Conor Durkan
Equally contributing authors, School of Informatics, University of Edinburgh
Equally contributing authors, School of Informatics, University of Edinburgh
Pedro J. Gonçalves
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn
David S. Greenberg
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Model-Driven Machine Learning, Centre for Materials and Coastal Research, Helmholtz-Zentrum Geesthacht
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Model-Driven Machine Learning, Centre for Materials and Coastal Research, Helmholtz-Zentrum Geesthacht
Jakob H. Macke
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Machine Learning in Science, University of Tübingen, Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Machine Learning in Science, University of Tübingen, Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen
Editor
Dan Foreman-Mackey