Lauritzen, SL;
Sadeghi, K;
(2018)
Unifying Markov Properties for Graphical Models.
Annals of Statistics
, 46
(5)
pp. 2251-2278.
10.1214/17-AOS1618.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Unifying Markov Properties for Graphical Models |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1214/17-AOS1618 |
| Publisher version: | https://doi.org/110.1214/17-AOS1618 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10058842 |
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