Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Kyle Cranmer, Mr. Prabhat, Frank Wood

NeurIPS 2019 pp. 5459-5472

/neurips/2019/baydin2019neurips-efficient/

Abstract

We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via inference compilation where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.

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Cite

Text

Baydin et al. "Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model." Neural Information Processing Systems, 2019.

Markdown

[Baydin et al. "Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model." Neural Information Processing Systems, 2019.](https://mlanthology.org/neurips/2019/baydin2019neurips-efficient/)

BibTeX

@inproceedings{baydin2019neurips-efficient,
  title     = {{Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model}},
  author    = {Baydin, Atilim Gunes and Shao, Lei and Bhimji, Wahid and Heinrich, Lukas and Naderiparizi, Saeid and Munk, Andreas and Liu, Jialin and Gram-Hansen, Bradley and Louppe, Gilles and Meadows, Lawrence and Torr, Philip and Lee, Victor and Cranmer, Kyle and Prabhat, Mr. and Wood, Frank},
  booktitle = {Neural Information Processing Systems},
  year      = {2019},
  pages     = {5459-5472},
  url       = {https://mlanthology.org/neurips/2019/baydin2019neurips-efficient/}
}