NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:2922
Title:Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

The paper presents a new probabilistic programming framework that makes Bayesian inference applicable to simulation code at scale. A large scale high energy physics application is presented. Probabilistic inference can be applied to an existing simulation code bass, allowing for ‘plug-and-play’ inference. A large-scale particle physics application was provided. On the downside, the involved inference approaches themselves have already been published before. Clarity on inference scheme could be improved; more technical details could be provided in the camera-ready version.