Efficient probabilistic inference in the quest for physics beyond the standard model

Efficient probabilistic inference in the quest for physics beyond the standard model

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...

書目詳細資料
Main Authors:	Baydin, AG, Heinrich, L, Bhimji, W, Shao, L, Naderiparizi, S, Munk, A, Liu, J, Gram-Hansen, B, Louppe, G, Meadows, L, Toor, P, Lee, V, Prabhat, Cranmer, K, Wood, F
格式:	Conference item
語言:	English
出版:	Neural Information Processing Systems 2019

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