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Learning marginal AMP chain graphs under faithfulness revisited
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
University of Granada, Spain.
2016 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 68, 108-126 p.Article in journal (Refereed) PublishedText
Abstract [en]

Marginal AMP chain graphs are a recently introduced family of models that is based on graphs that may have undirected, directed and bidirected edges. They unify and generalize the AMP and the multivariate regression interpretations of chain graphs. In this paper, we present a constraint based algorithm for learning a marginal AMP chain graph from a probability distribution which is faithful to it. We show that the marginal AMP chain graph returned by our algorithm is a distinguished member of its Markov equivalence class. We also show that our algorithm performs well in practice. Finally, we show that the extension of Meeks conjecture to marginal AMP chain graphs does not hold, which compromises the development of efficient and correct score+search learning algorithms under assumptions weaker than faithfulness. (C) 2015 Elsevier Inc. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC , 2016. Vol. 68, 108-126 p.
Keyword [en]
Chain graphs; AMP chain graphs; MVR chain graphs; Structure learning
National Category
Computer and Information Science
URN: urn:nbn:se:liu:diva-124106DOI: 10.1016/j.ijar.2015.09.004ISI: 000366774200009OAI: diva2:897174

Funding Agencies|Center for Industrial Information Technology [09.01]; Swedish Research Council [2010-4808]; Spanish Ministry of Economy and Competitiveness [TIN2013-46638-C3-2-P]; European Regional Development Fund (FEDER)

Available from: 2016-01-25 Created: 2016-01-19 Last updated: 2016-02-23

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Pena, Jose M
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