TY - GEN
T1 - Maximum a posteriori estimation by search in probabilistic programs
AU - Tolpin, David
AU - Wood, Frank
N1 - Publisher Copyright:
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85007259725
U2 - 10.1609/socs.v6i1.18369
DO - 10.1609/socs.v6i1.18369
M3 - Conference contribution
AN - SCOPUS:85007259725
T3 - Proceedings of the 8th Annual Symposium on Combinatorial Search, SoCS 2015
SP - 201
EP - 205
BT - Proceedings of the 8th Annual Symposium on Combinatorial Search, SoCS 2015
A2 - Lelis, Levi
A2 - Stern, Roni
PB - Association for the Advancement of Artificial Intelligence
T2 - 8th Annual Symposium on Combinatorial Search, SoCS 2015
Y2 - 11 June 2015 through 13 June 2015
ER -