Conference item

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 BaMC is faster and more robust on a range of probabilistic models.

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Tolpin, D., & Wood, F. (2015). Maximum a posteriori estimation by search in probabilistic programs. Proceedings of the Eighth International Symposium on Combinatorial Search, 201–205.

MLA Style

Tolpin, D., and F. Wood. “Maximum a Posteriori Estimation by Search in Probabilistic Programs.” Proceedings of the Eighth International Symposium on Combinatorial Search, AAAI Publications, 2015, pp. 201–05.

Chicago Style

Tolpin, D, and F Wood. 2015. “Maximum a Posteriori Estimation by Search in Probabilistic Programs.” In Proceedings of the Eighth International Symposium on Combinatorial Search, 201–5. AAAI Publications.
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Authors

+ Tolpin, D More by this author

Role:: Author

+ Wood, F More by this author

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
Host title:: Proceedings of the Eighth International Symposium on Combinatorial Search
Journal:: Proceedings of the Eighth International Symposium on Combinatorial Search More from this journal
Pages:: 201-205
Publication date:: 2015-01-01
Event location:: Ein Gedi, Israel

Keywords:: SBTMR
Pubs id:: pubs:687019
UUID:: uuid:554a0b51-9184-4396-9a48-819eee390c21
Local pid:: pubs:687019
Source identifiers:: 687019
Deposit date:: 2017-03-24

Terms of use

Copyright holder:: Association for the Advancement of Artificial Intelligence
Copyright date:: 2015
Notes:: Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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