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Learning AMP chain graphs and some marginal models thereof under faithfulness
Linköpings universitet, Institutionen för datavetenskap, Databas och informationsteknik. Linköpings universitet, Tekniska högskolan.
2014 (engelsk)Inngår i: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 55, nr 4, s. 1011-1021Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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 Meeks conjecture to AMP chain graphs does not hold, which compromises the development of efficient and correct score + search learning algorithms under assumptions weaker than faithfulness. We also study the problem of how to represent the result of marginalizing out some nodes in an AMP CG. We introduce a new family of graphical models that solves this problem partially. We name this new family maximal covariance-concentration graphs because it includes both covariance and concentration graphs as subfamilies.

sted, utgiver, år, opplag, sider
Elsevier , 2014. Vol. 55, nr 4, s. 1011-1021
Emneord [en]
AMP chain graphs; Chain graphs; Graphical models; Learning
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-106668DOI: 10.1016/j.ijar.2014.01.003ISI: 000334087400007OAI: oai:DiVA.org:liu-106668DiVA, id: diva2:718515
Tilgjengelig fra: 2014-05-21 Laget: 2014-05-19 Sist oppdatert: 2017-12-05

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