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Maximum a posteriori estimation by search in probabilistic programs

Abstract:

We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with varying number of mutually dependent finite, countable, and continuous random variables. BaMC is an anytime MAP search algorithm applicable to any combination of random variables and dependencies. We compare BaMC to other MAP estimation algorithms and show that B...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
Societies, Other & Subsidiary Companies
Department:
Kellogg College
Oxford college:
Kellogg College
Role:
Author
FA8750- 14-2-0004./Defense Advanced Research Projects Agency More from this funder
Publisher:
AAAI Publications Publisher's website
Journal:
Proceedings of the Eighth International Symposium on Combinatorial Search Journal website
Pages:
201-205
Host title:
Proceedings of the Eighth International Symposium on Combinatorial Search
Publication date:
2015-01-01
Event location:
Ein Gedi, Israel
Source identifiers:
687019
Keywords:
Pubs id:
pubs:687019
UUID:
uuid:554a0b51-9184-4396-9a48-819eee390c21
Local pid:
pubs:687019
Deposit date:
2017-03-24

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