Learning AMP Chain Graphs under Faithfulness

Details Learning AMP Chain Graphs under Faithfulness Learning AMP Chain Graphs under Faithfulness

2012-04-24

arxiv.org/abs/1204.5357v1

Statistics

Machine Learning

Scientific paper

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.

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