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Learning Marginal AMP Chain Graphs under Faithfulness

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Probabilistic Graphical Models (PGM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8754))

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Abstract

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 also show that the extension of Meek’s 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.

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Author information

Authors and Affiliations

  1. ADIT, IDA, Linköping University, SE-58183, Linköping, Sweden

    Jose M. Peña

Authors
  1. Jose M. Peña
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Editor information

Editors and Affiliations

  1. Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC Utrecht, The Netherlands

    Linda C. van der Gaag

  2. Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC Utrecht,, The Netherlands

    Ad J. Feelders

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© 2014 Springer International Publishing Switzerland

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Peña, J.M. (2014). Learning Marginal AMP Chain Graphs under Faithfulness. In: van der Gaag, L.C., Feelders, A.J. (eds) Probabilistic Graphical Models. PGM 2014. Lecture Notes in Computer Science(), vol 8754. Springer, Cham. https://doi.org/10.1007/978-3-319-11433-0_25

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  • DOI: https://doi.org/10.1007/978-3-319-11433-0_25

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11432-3

  • Online ISBN: 978-3-319-11433-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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