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Bayesian Optimization for Probabilistic Programs

Abstract:

We outline a general purpose framework for black-box marginal maximum a pos- teriori estimation of probabilistic program variables using Bayesian optimization with Gaussian processes. We introduce the concept of an optimization query, whereby a probabilistic program returns an infinite lazy sequence of increasingly optimal estimates, and explain how a general purpose program transformation would allow the evidence of any probabilistic program, and therefore any graphical model, to be optimize...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Department:
Oxford, MPLS, Engineering Science
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Author
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Department:
Oxford, MPLS, Engineering Science
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Author
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Department:
Exeter College
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Author
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Department:
Oxford, MPLS, Engineering Science
Role:
Author
Publisher:
Neural Information Processing Systems Foundation Publisher's website
Publication date:
2016-12-05
Acceptance date:
2016-05-20
Pubs id:
pubs:664824
URN:
uri:cf44b369-52b5-40fc-bc3d-b4e3f69bf86c
UUID:
uuid:cf44b369-52b5-40fc-bc3d-b4e3f69bf86c
Local pid:
pubs:664824

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