Learning marginal AMP chain graphs under faithfulness revisited
نویسندگان
چکیده
منابع مشابه
Learning marginal AMP chain graphs under faithfulness revisited
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 ...
متن کاملLearning Marginal AMP Chain Graphs under Faithfulness
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 ...
متن کاملLearning AMP Chain Graphs under Faithfulness
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+sea...
متن کاملLearning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness
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 Meek’s conjecture to AMP chain graphs does not hold, which compromises the development of efficient and correct score+search lea...
متن کاملLearning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness: Extended Version
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 Meek’s conjecture to AMP chain graphs does not hold, which compromises the development of efficient and correct score+search lea...
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ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2016
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2015.09.004