https://github.com/bayesflow-org/jana-paper

Contains the code accompanying the paper "JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models"

https://github.com/bayesflow-org/jana-paper

Repository

Contains the code accompanying the paper "JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models"

https://github.com/bayesflow-org/JANA-Paper/blob/main/

# JANA



This repository contains the code for running and reproducing the experiments from the paper [JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models](https://arxiv.org/abs/2302.09125), presented at the Conference on Uncertainty in Artificial Intelligence (UAI 2023).

JANA lets you train and validate specialized neural networks for simultaneously amortized simulation-based inference and surrogate modeling in a Bayesian framework. The method is described in our paper:

Radev, S. T., Schmitt, M., Pratz, V., Picchini, U., Kthe, U., & Brkner, P. C. (2023). 
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models. 
arXiv preprint arXiv:2302.09125, available for free at: https://arxiv.org/abs/2302.09125.

The code depends on the [BayesFlow](https://github.com/stefanradev93/BayesFlow) library, which implements all JANA components and benchmark simulators. Each experiment features self-contained code and individual instructions. Checkpoints for most networks are provided at the cost of the repository's size.

## Cite

You can easily cite the proceedings paper as:
```bibtex
@InProceedings{radev2023jana,
  title = 	 {{JANA}: Jointly amortized neural approximation of complex {B}ayesian models},
  author =       {Radev, Stefan T. and Schmitt, Marvin and Pratz, Valentin and Picchini, Umberto and K\"othe, Ullrich and B\"urkner, Paul-Christian},
  booktitle = 	 {Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {1695--1706},
  year = 	 {2023},
  volume = 	 {216},
  series = 	 {Proceedings of Machine Learning Research},
  publisher =    {PMLR},
}
```

## Support

This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germanys Excellence Strategy - EXC-2181 - 390900948 (the Heidelberg Cluster of Excellence STRUCTURES) and -- EXC-2075 - 390740016 (the Stuttgart Cluster of Excellence SimTech), the Cyber Valley Research Fund (grant number: CyVy-RF-2021-16), the Swedish National Research Council (Vetenskapsrdet 2019-03924), the Chalmers AI Research Centre, the Informatics for Life initiative funded by the Klaus Tschira Foundation and Google Cloud through the Academic Research Grants program.

## License

MIT

Owner

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