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

arXiv: https://arxiv.org/abs/1807.07706 · HTML: https://mlsanity.com/paper/1807.07706/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Variational Inference for Deblending Crowded Starfields](https://mlsanity.com/paper/2102.02409/) (2021)
- [Uncertain Evidence in Probabilistic Models and Stochastic Simulators](https://mlsanity.com/paper/2210.12236/) (2022)
- [Flow Matching for Posterior Inference with Simulator Feedback](https://mlsanity.com/paper/2410.22573/) (2024)
- [Meta-Learning an Inference Algorithm for Probabilistic Programs](https://mlsanity.com/paper/2103.00737/) (2021)
- [Semi-Amortized Bayesian Inference with Reusable Local Posteriors](https://mlsanity.com/paper/2610.18945/) (2026)

## Abstract (arXiv)

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.