PPX is a cross-platform Probabilistic Programming eXecution protocol and API based on flatbuffers. It is intended as an open interoperability protocol between models and inference engines implemented in different probabilistic programming languages. PPX is related to pyprob, a PyTorch-based library for probabilistic programming and inference compilation. See Atılım Güneş Baydin’s keynote talk at ACAT2019.
This work led to the following publicatoins:
“Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model”, arXiv:1807.07706 ) published in NeurIPS2019.
“Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale”, A. Baydin, L. Shao, W. Bhimji, L. Heinrich, L. Meadows et. al., published in SC19 arXiv:1907.03382 https://dl.acm.org/doi/10.1145/3295500.3356180 (07 Jul 2019)
PPX protocol and
pyprob tool have since been applied to epidemiological studies such as Malaria and COVID19:
- “Hijacking Malaria Simulators with Probabilistic Programming”, arXiv:1905.12432
- “Planning as inference in epidemiological dynamics models” by Warrington, A., Naderiparizi, S., Weilbach, C., Masrani, V., Harvey, W., Scibior, A., Beronov, B., & Nasseri, A. (2020) arXiv:2003.13221
- “Simulation-Based Inference for Global Health Decisions” by Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, Rob Zinkov, Puneet Dokania, N. Siddharth, Ana Belen Espinosa-Gonzalez, Ara Darzi, Philip Torr, and Atılım Güneş Baydin arXiv:2005.07062.
These tools are now also being used to extend Dark Matter astrophysics by instrumenting the simulation of strong gravitational lensing used in Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
- Kyle Cranmer
- Atılım Güneş Baydin
- Tuan Anh Le
- Lukas Heinrich
- Wahid Bhimji
- Kyle Cranmer
- Frank Wood
- Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale, Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19), November 17--22, 2019 DOI:10.1145/3295500.3356180 (07 Jul 2019) [3 citations] [NSF PAR].
- Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model, Advances in Neural Information Processing Systems 33 (NeurIPS) (20 Jul 2018) [3 citations].