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?

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.

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.

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. Papers published at the Neural Information Processing Systems Conference.

Over the last couple of years, the interest in combining probability and logic has grown strongly. This led to the development of different software packages like PRISM, ProbLog and Alchemy, which offer a variety of exact and approximate algorithms to perform inference and learning. What is lacking, however, are algorithms to perform efficient inference in relational temporal models by systematically exploiting temporal and local structure. Since many real-world problems require temporal models, we argue that more research is necessary to use this structure to obtain more efficient inference and learning. While existing relational representations of dynamic domains focus rather on approximate inference techniques we propose an exact algorithm.