Uncertainty in Artificial Intelligence
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Markov Equivalence Classes for Maximal Ancestral Graphs
Ayesha Ali, Thomas Richardson
Abstract:
Ancestral graphs are a class of graphs that encode conditional independence relations arising in DAG models with latent and selection variables, corresponding to marginalization and conditioning. However, for any ancestral graph, there may be several other graphs to which it is Markov equivalent. We introduce a simple representation of a Markov equivalence class of ancestral graphs, thereby facilitating model search. \ More specifically, we define a join operation on ancestral graphs which will associate a unique graph with a Markov equivalence class. We also extend the separation criterion for ancestral graphs (which is an extension of d-separation) and provide a proof of the pairwise Markov property for joined ancestral graphs.
Keywords:
Pages: 1-9
PS Link:
PDF Link: /papers/02/p1-ali.pdf
BibTex:
@INPROCEEDINGS{Ali02,
AUTHOR = "Ayesha Ali and Thomas Richardson",
TITLE = "Markov Equivalence Classes for Maximal Ancestral Graphs",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "1--9"
}


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