Graphoids are topological invariants of virtual graph diagrams.
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Let be a countable family of rational functions of two variables with real coefficients. Each rational function can be thought as a continuous function taking values in the projective line and defined on a cofinite subset of the torus . Then t…
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 -separation on such graphs are compositional graphoids. We focus in particular on the subclass of rib…
We present a graphical criterion for reading dependencies from the minimal directed independence map G of a graphoid p when G is a polytree and p satisfies composition and weak transitivity. We prove that the criterion is sound and complete. We argue that assuming composition and weak transitivity is not too restrictiv…
The covariance graph (aka bi-directed graph) of a probability distribution is the undirected graph where two nodes are adjacent iff their corresponding random variables are marginally dependent in . In this paper, we present a graphical criterion for reading dependencies from , under the assumption that $…
We present a new family of models that is based on graphs that may have undirected, directed and bidirected edges. We name these new models marginal AMP (MAMP) chain graphs because each of them is Markov equivalent to some AMP chain graph under marginalization of some of its nodes. However, MAMP chain graphs do not onl…
A new graphical model for discrete data without parametric restrictions.
This paper deals with chain graphs under the Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability distribution is faithful to. Moreover, we show that the extension of Meek's conjecture to AMP chain graphs does not hold…