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

Neural Information Processing Systems 

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.