UNIFYING MARKOV PROPERTIES FOR GRAPHICAL MODELS

• 成果类型: Article
• 署名作者: Lauritzen, Steffen; Sadeghi, Kayvan
• 署名单位: University of Copenhagen; University of Cambridge
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/17-AOS1618
• 发表日期: 2018
• 页码: 2251-2278
• 关键词: conditional-independence

摘要

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.