Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Management Science
An algorithm for deciding if a set of observed independencies has a causal explanation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Chain graphs: semantics and expressiveness
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Directed cyclic graphical representations of feedback models
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Separoids: A Mathematical Framework for Conditional Independence and Irrelevance
Annals of Mathematics and Artificial Intelligence
A Graphical Representation of Equivalence Classes of AMP Chain Graphs
The Journal of Machine Learning Research
Parallell interacting MCMC for learning of topologies of graphical models
Data Mining and Knowledge Discovery
Graphical models and exponential families
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Cost-sharing in Bayesian knowledge bases
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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Graphical Markov models use graphs, either undirected, directed, or mixed, to represent possible dependences among statistical variables. Applications of undirected graphs (UDGs) include models for spatial dependence and image analysis, while acyctic directed graphs (ADGs), which are especially convenient for statistical analysis, arise in such fields as genetics and psychometrics and as models for expert systems and Bayesian belief networks. Lauritzen, Wermuth, and Frydenberg (LWF) introduced a Markov property for chain graphs, which are mixed graphs that can be used to represent simultaneously both causal and associative dependencies and which include both UDGs and ADGs as special cases. In this paper an alternative Markov property (AMP) for chain graphs is introduced, which in some ways is a more direct extension of the ADG Markov property than is the LWF property for chain graph.