Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Semigraphoids and structures of probabilistic conditional independence
Annals of Mathematics and Artificial Intelligence
Conditional Independence in A Coherent Finite Setting
Annals of Mathematics and Artificial Intelligence
Conditional independence structure and its closure: Inferential rules and algorithms
International Journal of Approximate Reasoning
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
On the implication problem for probabilistic conditional independency
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Acyclic directed graphs representing independence models
International Journal of Approximate Reasoning
Exploiting independencies to compute semigraphoid and graphoid structures
International Journal of Approximate Reasoning
Finding P-maps and I-maps to represent conditional independencies
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
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In this paper we consider conditional independence models closed under graphoid properties. We investigate their representation by means of acyclic directed graphs (DAG). A new algorithm to build a DAG, given an ordering among random variables, is described and peculiarities and advantages of this approach are discussed. Finally, some properties ensuring the existence of perfect maps are provided. These conditions can be used to define a procedure able to find a perfect map for some classes of independence models.