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

The main contributions of this work are pulling these ideas together into a practical framework that works on a real large-scale simulator. The original challenges that are addressed include: how to apply PPL to an existing code base? The other strength of the paper is the sheer depth of related work that is considered and explained, while being smooth to read at the same time. Ideally, we would have had more detail on the specific contributions of this paper, particularly on the "prior inflation" scheme and the protocol. The limitations of the writing come mainly from needing further explanation and discussion for why various ideas are being used, e.g., why do you consider LMH, RMH, IC? why would you "like to employ deep neural networks" in this context?