Machine Learning

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

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

    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.

    Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
    by Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio, Tuan Anh Le, Frank Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat
    https://arxiv.org/pdf/1712.07901v1.pdf

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