Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Boolean Feature Discovery in Empirical Learning
Machine Learning
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
Classifiers: a theoretical and empirical study
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
On Stochastic Conditional Independence: the Problems of Characterization and Description
Annals of Mathematics and Artificial Intelligence
Parallel Learning of Belief Networks in Large and Difficult Domains
Data Mining and Knowledge Discovery
Learning Pseudo-independent Models: Analytical and Experimental Results
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Local Score Computation in Learning Belief Networks
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Complexity measurement of fundamental pseudo-independent models
International Journal of Approximate Reasoning
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Mind change optimal learning of Bayes net structure from dependency and independency data
Information and Computation
Basic principles of learning Bayesian logic programs
Probabilistic inductive logic programming
Exploring parallelism in learning belief networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning belief networks in domains with recursively embedded pseudo independent submodels
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Structure and parameter learning for causal independence and causal interaction models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Foundation for the new algorithm learning pseudo-independent models
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
Conservative independence-based causal structure learning in absence of adjacency faithfulness
International Journal of Approximate Reasoning
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In learning belief networks, the single link lookahead search is widely adopted to reduce the search space. We show that there exists a class of probabilistic domain models which displays a special pattern of dependency. We analyze the behavior of several learning algorithms using different scoring metrics such as the entropy, conditional independence, minireal description length and Bayesian metrics. We demonstrate that single link lookahead search procedures (employed in these algorithms) cannot learn these models correctly. Thus, when the underlying domain model actually belongs to this class, the use of a single link search procedure will result in learning of an incorrect model. This may lead to inference errors when the model is used. Our analysis suggests that if the prior knowledge about a domain does not rule out the possible existence of these models, a multilink lookahead search or other heuristics should be used for the learning process.