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Learning AMP Chain Graphs under Faithfulness (1204.5357v1)
Published 24 Apr 2012 in stat.ML, cs.AI, math.ST, and stat.TH
Abstract: This paper deals with chain graphs under the alternative 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. We also show that the extension of Meek's 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.