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Markov properties for mixed graphs
In this paper, we unify the Markov theory of a variety of different type...
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Priors on exchangeable directed graphs
Directed graphs occur throughout statistical modeling of networks, and e...
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V1: A Visual Query Language for Property Graphs
V1 is a declarative visual query language for schema-based property grap...
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The evolution of the global Markov property for multivariate regression graphs: differences and conflicts
Depending on the interpretation of the type of edges, a chain graph can ...
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Causal Networks: Semantics and Expressiveness
Dependency knowledge of the form "x is independent of y once z is known"...
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Mixed Graphical Models for Causal Analysis of Multi-modal Variables
Graphical causal models are an important tool for knowledge discovery be...
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Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs
We extend Andersson-Madigan-Perlman chain graphs by (i) relaxing the sem...
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An Alternative Markov Property for Chain Graphs
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 acyclic 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.
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