Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Inference of structures of models of probabilistic dependences from statistical data
Cybernetics and Systems Analysis
Learning Bayesian Networks
Strong completeness and faithfulness in Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Large-sample learning of bayesian networks is NP-hard
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Construction of minimal d-separators in a dependency system
Cybernetics and Systems Analysis
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This paper considers some properties of (locally) minimal separators in oriented graphical dependency models, i.e., in Bayesian networks, Gaussian networks, and hybrid networks. Statements and rules are inferred from the criterion of d-separation and acyclic property of digraph. Necessary conditions are established that should be satisfied by members of (locally) minimal separators. Patterns of evidences are found that allow one to identify the presence or absence of an edge without an extensive search for a separator. These means facilitate the efficient inference of a model structure with the help of constraint-based algorithms.