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A convenient category for higher-order probability theory

Abstract:

Higher-order probabilistic programming languages allow programmers to write sophisticated models in machine learning and statistics in a succinct and structured way, but step outside the standard measure-theoretic formalization of probability theory. Programs may use both higher-order functions and continuous distributions, or even define a probability distribution on functions. But standard probability theory does not handle higher-order functions well: the category of measurable spaces is n...

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

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Publisher copy:
10.1109/LICS.2017.8005137

Authors


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Department:
Oxford, MPLS, Computer Science
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Department:
Oxford, MPLS, Computer Science
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Department:
Oxford, MPLS, Computer Science
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Grant:
Research Fellowship
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Grant:
No.R0190-16- 2011, Development of Vulnerability Discovery Technologies for IoT Software Security
Publisher:
IEEE Publisher's website
Publication date:
2017-08-18
Acceptance date:
2017-03-22
DOI:
ISSN:
1043-6871
Pubs id:
pubs:690035
URN:
uri:0e477a89-389f-4a4d-8dc2-7a86d525552e
UUID:
uuid:0e477a89-389f-4a4d-8dc2-7a86d525552e
Local pid:
pubs:690035
ISBN:
978-1-5090-3019-4

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