Probabilistic Conditional Independence Structures: With 42 Illustrations (Information Science and Statistics)
The Journal of Machine Learning Research
Bayesian networks from the point of view of chain graphs
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Strong completeness and faithfulness in Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Reading dependencies from covariance graphs
International Journal of Approximate Reasoning
Qualitative chain graphs and their application
International Journal of Approximate Reasoning
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This paper deals with chain graphs under the classic Lauritzen-Wermuth-Frydenberg interpretation. We prove that the strictly positive discrete probability distributions with the prescribed sample space that factorize according to a chain graph G with dimension d have positive Lebesgue measure wrt R^d, whereas those that factorize according to G but are not faithful to it have zero Lebesgue measure wrt R^d. This means that, in the measure-theoretic sense described, almost all the strictly positive discrete probability distributions with the prescribed sample space that factorize according to G are faithful to it.