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IJAR
2016

On expressiveness of the chain graph interpretations

3 years 9 months ago
On expressiveness of the chain graph interpretations
In this article we study the expressiveness of the different chain graph interpretations. Chain graphs is a class of probabilistic graphical models that can contain two types of edges, representing different types of relationships between the variables in question. Chain graphs is also a superclass of directed acyclic graphs, i.e. Bayesian networks, and can thereby represent systems more accurately than this less expressive class of models. Today there do however exist several different ways of interpreting chain graphs and what conditional independences they encode, giving rise to different so called chain graph interpretations. Previous research has approximated the number of representable independence models for the Lauritzen-Wermuth-Frydenberg and the multivariate regression chain graph interpretations using an MCMC based approach. In this article we use a similar approach to approximate the number of models representable by the latest chain graph interpretation in research, t...
Dag Sonntag, Jose M. Peña
Added 04 Apr 2016
Updated 04 Apr 2016
Type Journal
Year 2016
Where IJAR
Authors Dag Sonntag, Jose M. Peña
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