UNIFYING MARKOV PROPERTIES FOR GRAPHICAL MODELS

作者：Lauritzen Steffen^*; Sadeghi Kayvan

来源：Annals of Statistics, 2018, 46(5): 2251-2278.

DOI：10.1214/17-AOS1618

• AMP Markov property
• c-separation
• chain graph
• compositional graphoid
• d-separation
• independence model
• LWF Markov property
• m-separation
• mixed graph
• pairwise Markov property
• regression chain Markov property

Several types of graphs with different conditional independence interpretations-also known as Markov properties-have been proposed and used in graphical models. In this paper, we unify these Markov properties by introducing a class of graphs with four types of edges-lines, arrows, arcs and dotted lines-and a single separation criterion. We show that independence structures defined by this class specialize to each of the previously defined cases, when suitable subclasses of graphs are considered. In addition, we define a pairwise Markov property for the subclass of chain mixed graphs, which includes chain graphs with the LWF interpretation, as well as summary graphs (and consequently ancestral graphs). We prove the equivalence of this pairwise Markov property to the global Markov property for compositional graphoid independence models.

• 出版日期2018-10