@inproceedings{5fb264ef75fb47e7961a0b3a18d9cabf,
title = "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.",
author = "David Tolpin and Frank Wood",
note = "Funding Information: This work is supported under DARPA PPAML through the U.S. AFRL under Cooperative Agreement number FA8750-14-2-0004. Publisher Copyright: Copyright {\textcopyright} 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 8th Annual Symposium on Combinatorial Search, SoCS 2015 ; Conference date: 11-06-2015 Through 13-06-2015",
year = "2015",
month = jan,
day = "1",
language = "English",
series = "Proceedings of the 8th Annual Symposium on Combinatorial Search, SoCS 2015",
publisher = "AAAI press",
pages = "201--205",
editor = "Levi Lelis and Roni Stern",
booktitle = "Proceedings of the 8th Annual Symposium on Combinatorial Search, SoCS 2015",
}