By Topic

Probability bounds for goal directed queries in Bayesian networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Mannino, M.V. ; Graduate Sch. of Bus. Adm., Colorado Univ., Denver, CO, USA ; Mookerjee, V.S.

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.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:14 ,  Issue: 5 )