DocumentCode :
823568
Title :
Probability bounds for goal directed queries in Bayesian networks
Author :
Mannino, Michael V. ; Mookerjee, Vijay S.
Author_Institution :
Graduate Sch. of Bus. Adm., Colorado Univ., Denver, CO, USA
Volume :
14
Issue :
5
fYear :
2002
Firstpage :
1196
Lastpage :
1200
Abstract :
We derive bounds on the probability of a goal node given a set of acquired input nodes. The bounds apply to decomposable networks; a class of Bayesian networks encompassing causal trees and causal polytrees. The difficulty of computing the bounds depends on the characteristics of the decomposable network. For directly connected networks with binary goal nodes, tight bounds can be computed in polynomial time. For other kinds of decomposable networks, the derivation of the bounds requires solving an integer program with a nonlinear objective function, a computationally intractable problem in the worst case. We provide a relaxation technique that computes looser bounds in polynomial time for more complex decomposable networks. We briefly describe an application of the probability bounds to a record linkage problem.
Keywords :
belief networks; computational complexity; integer programming; probability; query processing; trees (mathematics); Bayesian networks; binary goal nodes; causal polytrees; causal trees; decomposable networks; goal directed queries; integer program; nonlinear objective function; polynomial time; probability bounds; record linkage problem; relaxation technique; sequential decision making; tight bounds; Bayesian methods; Communication industry; Computer networks; Costs; Couplings; Decision making; Engines; Intelligent networks; Polynomials;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2002.1033865
Filename :
1033865
Link To Document :
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