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Models of complex systems are often formalized as sequential softwaresimulators: computationally intensive programs that iteratively build upprobable system configurations given parameters and initial conditions. Thesesimulators enable modelers to capture effects that are difficult tocharacterize analytically or summarize statistically. However, in manyreal-world applications, these simulations need to be inverted to match theobserved data. This typically requires the custom design, derivation andimplementation of sophisticated inversion algorithms. Here we give a frameworkfor inverting a broad class of complex software simulators via probabilisticprogramming and automatic inference, using under 20 lines of probabilisticcode. Our approach is based on a formulation of inversion as approximateinference in a simple sequential probabilistic model. We implement fourinference strategies, including Metropolis-Hastings, a sequentializedMetropolis-Hastings scheme, and a particle Markov chain Monte Carlo scheme,requiring 4 or fewer lines of probabilistic code each. We demonstrate ourframework by applying it to invert a real geological software simulator fromthe oil and gas industry.

Automatic Inference for Inverting Software Simulators via Probabilistic Programming