Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

Abstract

We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from population biology to cosmology, low-level mechanistic components are composed to create complex generative models. These models lead to intractable likelihoods and are typically non-differentiable, which poses challenges for traditional approaches to inference. We extend previous work in "inference compilation", which combines universal probabilistic programming and deep learning methods, to large-scale scientific simulators, and introduce a C++ based probabilistic programming library called CPProb. We successfully use CPProb to interface with SHERPA, a large code-base used in particle physics. Here we describe the technical innovations realized and planned for this library.

7 pages, 2 figures

References in corpus (7)

• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• Event generation with SHERPA 1.1
• Approximate Bayesian computation (ABC) gives exact results under the assumption of model error
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Cited by in corpus (4)

• Machine learning and the physical sciences
• Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
• Genetic algorithms with DNN-based trainable crossover as an example of partial specialization of general search
• Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model