  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.
