Markov properties for mixed graphs

  title={Markov properties for mixed graphs},
  author={Kayvan Sadeghi and Steffen L. Lauritzen},
In this paper, we unify the Markov theory of a variety of different types of graphs used in graphical Markov models by introducing the class of loopless mixed graphs, and show that all independence models induced by m-separation on such graphs are compositional graphoids. We focus in particular on the subclass of ribbonless graphs which as special cases include undirected graphs, bidirected graphs, and directed acyclic graphs, as well as ancestral graphs and summary graphs. We define maximality… CONTINUE READING


Publications referenced by this paper.
Showing 1-10 of 36 references

Ancestral Graph Markov Models

View 9 Excerpts
Highly Influenced

Graphical Models. Oxford Statistical Science Series 17

S. L. Lauritzen
View 7 Excerpts
Highly Influenced

Marginalizing and conditioning in graphical models. Bernoulli 8 817–840

J.T.A. Koster
View 4 Excerpts
Highly Influenced

probabilistic reasoning in intelligent systems: networks of plausible inference san mateo

Morgan Kaufmann series in representation and reasoning • 1988
View 5 Excerpts
Highly Influenced

Markov Properties for Linear Causal Models with Correlated Errors

Journal of Machine Learning Research • 2009
View 1 Excerpt

Binary models for marginal independence

M. Drton, T. S. Richardson
J. R. Stat. Soc. Ser. B Stat. Methodol • 2008
View 1 Excerpt

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