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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.
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Improvements to inference compilation for probabilistic programming in large-scale scientific simulators
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Masked autoregressive flow for density estimation
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Automatic differentiation in PyTorch
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Hierarchical implicit models and likelihood-free variational inference
D. Tran, R. Ranganath, and D. Blei · 2017
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Jet Substructure at the Large Hadron Collider : Experimental Review
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