sbi reloaded: a toolkit for simulation-based inference workflows
Creators
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Boelts, Jan
(Project leader)
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Deistler, Michael
(Project leader)
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Glöckler, Manuel1
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Tejero-Cantero, Alvaro
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Lueckmann, Jan-Matthis
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Moss, Guy1
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Steinbach, Peter2
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Durkan, Conor
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Moreau, Thomas3
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Linhart, Julia
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Muratore, Fabio
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Vetter, Julius
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Miller, Benjamin4
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Herold, Maternus
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Ziaeemehr, Abolfazl
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Pals, Matthijs1
- Gruner, Theo
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Bischoff, Sebastian1
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Krouglova, Anastasia
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Gao, Richard1
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Lappalainen, Janne
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Mucsányi, Bálint
- Pei, Felix
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Schulz, Auguste1
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Stefanidi, Zinovia
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Rodrigues, Pedro L. C.5, 3
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Schröder, Cornelius1
- Abu Zaid, Faried
- Beck, Jonas
- Kapoor, Jaivardhan
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Greenberg, David
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Gonçalves, Pedro
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Macke, Jakob
- 1. University of Tübingen
-
2.
Helmholtz-Zentrum Dresden-Rossendorf
- 3. Inria Centre de Recherche Saclay-Île-de-France
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4.
University of Amsterdam
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5.
Centre Inria de l'Université Grenoble Alpes
Description
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data.
Files
sbi-dev/sbi-v0.24.0.zip
Files
(6.3 MB)
| Name | Size | Download all |
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md5:a53a548ce8a8fd7cb99facfe12d065d2
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Additional details
Related works
- Is supplement to
- Software: https://github.com/sbi-dev/sbi/tree/v0.24.0 (URL)
Software
- Repository URL
- https://github.com/sbi-dev/sbi
- Programming language
- Python