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

Abstract:

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. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-9854-8100
Publisher:
Neural Information Processing Systems
Journal:
NIPS Proceedings More from this journal
Article number:
8785
Publication date:
2019-12-10
Acceptance date:
2019-09-04
Event title:
Advances in Neural Information Processing Systems 32 (NIPS 2019)
Event location:
Vancouver, Canada
Event website:
https://nips.cc/Conferences/2019
Event start date:
2019-12-08
Event end date:
2019-12-14
Language:
English
Keywords:
Pubs id:
1115298
Local pid:
pubs:1115298
Deposit date:
2020-07-01

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