Reference : Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
E-prints/Working papers : Already available on another site
Engineering, computing & technology : Computer science
Physical, chemical, mathematical & earth Sciences : Physics
http://hdl.handle.net/2268/226859
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
English
Gunes Baydin, Atilim [> >]
Heinrich, Lukas [> >]
Bhimji, Wahid [> >]
Gram-Hansen, Bradley [> >]
Louppe, Gilles mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data >]
Shao, Lei [> >]
Prabhat [> >]
Cranmer, Kyle [> >]
Wood, Frank [> >]
20-Jul-2018
No
[en] Computer Science - Machine Learning ; High Energy Physics - Phenomenology ; Physics - Data Analysis ; Statistics and Probability ; Statistics - Machine Learning ; 68T37 ; 68T05 ; 62P35 ; G.3 ; I.2.6 ; J.2
[en] We present a novel framework that enables efficient probabilistic inference in large-scale scientific models by allowing the execution of existing domain-specific simulators as probabilistic programs, resulting in highly interpretable posterior inference. Our framework is general purpose and scalable, and is based on a cross-platform probabilistic execution protocol through which an inference engine can control simulators in a language-agnostic way. 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. High-energy physics has a rich set of simulators based on quantum field theory and the interaction of particles in matter. We show how to use probabilistic programming to perform Bayesian inference in these existing simulator codebases directly, in particular conditioning on observable outputs from a simulated particle detector to directly produce an interpretable posterior distribution over decay pathways. 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 Markov chain Monte Carlo sampling.
Researchers
http://hdl.handle.net/2268/226859
18 pages, 5 figures
https://arxiv.org/abs/1807.07706

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