Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Applying inductive learning to enhance knowledge-based expert systems
Decision Support Systems
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
An Empirical Evaluation of a Randomized Algorithm for Probabilistic Inference
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
an entropy-driven system for construction of probabilistic expert systems from databases
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
ACM Computing Surveys (CSUR)
Probabilistic and analytical estimation of software development team size
International Journal of Hybrid Intelligent Systems
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
An optimization-based approach for the design of Bayesian networks
Mathematical and Computer Modelling: An International Journal
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We present a technique to construct efficient belief network structures for application areas where large amounts of data are available and information on the ordering of the variables can be obtained from domain experts. We identify classes of networks that are efficient for propagating beliefs. We formulate the problem as one of determining the belief network representation from a given class that best represents the data. We use the I-Divergence measure which is known to have certain desirable properties for evaluating different approximations. We present some theoretical findings that characterize the nature of solutions that are obtained. These theoretical results lead to an efficient solution procedure for finding the best network representation. We also discuss other information that may be reasonably obtained from experts, and show how such information leads to improving the efficiency of the technique to find the best network structure.