Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Conditional independence in uncertainty theories
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
On the Desirability of Acyclic Database Schemes
Journal of the ACM (JACM)
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
Conditional independence structures examined via minors
Annals of Mathematics and Artificial Intelligence
A hypergraph-theoretic analysis of collapsibility and decomposability for extended log-linear models
Statistics and Computing
Structure learning with nonparametric decomposable models
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Algorithms for learning decomposable models and chordal graphs
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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Decomposable dependency models possess a number of interesting and useful properties. This paper presents new characterizations of decomposable models in terms of independence relationships, which are obtained by adding a single axiom to the well-known set characterizing dependency models that are isomorphic to undirected graphs. We also briefly discuss a potential application of our results to the problem of learning graphical models from data.