Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Discovering Temporal Rules from Temporally Ordered Data
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Probabilistic Methods for Financial and Marketing Informatics
Probabilistic Methods for Financial and Marketing Informatics
Most probable explanations in Bayesian networks: Complexity and tractability
International Journal of Approximate Reasoning
A case study in knowledge discovery and elicitation in an intelligent tutoring application
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
An ontology-based approach for constructing Bayesian networks
Data & Knowledge Engineering
Hydrologic models for emergency decision support using bayesian networks
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Properties of Bayesian student model for INQPRO
Applied Intelligence
An optimization-based approach for the design of Bayesian networks
Mathematical and Computer Modelling: An International Journal
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In this paper we examine the use of Bayesian networks (BNs) for improving weather prediction, applying them to the problem of predicting sea breezes. We compare a pre-existing Bureau of Meteorology rule-based system with an elicited BN and others learned by two data mining programs, TETRAD II [Spirtes et al., 1993] and Causal MML [Wallace and Korb, 1999]. These Bayesian nets are shown to significantly outperform the rule-based system in predictive accuracy.