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

By Atilim Gunes Baydin, Lukas Heinrich, Wahid Bhimji, Andreas Munk, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank D. Wood. 2018

33 citations recorded by the source corpus.

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.

Subfields: Hardware and Architecture, Information Systems and Management, Management Science and Operations Research, Probabilistic & Bayesian Methods

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Citations in the corpus

An Introduction to Probabilistic Programming (2018, background): “One exciting example application under consideration at the time of writing is to instrument the stochastic simulators that simulate the standard model and the detectors employed by the large hadron collider (Baydin et al., 2018).”
Meta-Learning an Inference Algorithm for Probabilistic Programs (2021, background): “…of probabilistic programming is to automate reasoning about probabilistic models from diverse domains (Ritchie et al., 2015; Perov & Wood, 2016; Baydin et al., 2019; Schaechtle et al., 2016; Cusumano-Towner et al., 2017; Saad & Mansinghka, 2016; Kulkarni et al., 2015; Young et al., 2019; Jäger…”
Deep Probabilistic Surrogate Networks for Universal Simulator Approximation (2019, background): “PSN targets universal PPLs, so we focus our discussion here on those.”