Maximum a posteriori estimation by search in probabilistic programs

David Tolpin, Frank Wood

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of the 8th Annual Symposium on Combinatorial Search, SoCS 2015
EditorsLevi Lelis, Roni Stern
PublisherAAAI press
Pages201-205
Number of pages5
ISBN (Electronic)9781577357322
StatePublished - 1 Jan 2015
Externally publishedYes
Event8th Annual Symposium on Combinatorial Search, SoCS 2015 - Ein Gedi, Israel
Duration: 11 Jun 201513 Jun 2015

Publication series

NameProceedings of the 8th Annual Symposium on Combinatorial Search, SoCS 2015
Volume2015-January

Conference

Conference8th Annual Symposium on Combinatorial Search, SoCS 2015
Country/TerritoryIsrael
CityEin Gedi
Period11/06/1513/06/15

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