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A Dynamic Programming Algorithm for Learning Chain Event Graphs

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Discovery Science (DS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8140))

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Abstract

Chain event graphs are a model family particularly suited for asymmetric causal discrete domains. This paper describes a dynamic programming algorithm for exact learning of chain event graphs from multivariate data. While the exact algorithm is slow, it allows reasonably fast approximations and provides clues for implementing more scalable heuristic algorithms.

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Author information

Authors and Affiliations

  1. National University of Singapore, Singapore

    Tomi Silander & Tze-Yun Leong

Authors
  1. Tomi Silander
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  2. Tze-Yun Leong
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Editor information

Editors and Affiliations

  1. TU Darmstadt, Germany

    Johannes Fürnkranz

  2. Phillips-Universität Marburg, Germany

    Eyke Hüllermeier

  3. The Institute of Statistical Mathematics, Tokyo, Japan

    Tomoyuki Higuchi

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Silander, T., Leong, TY. (2013). A Dynamic Programming Algorithm for Learning Chain Event Graphs. In: Fürnkranz, J., Hüllermeier, E., Higuchi, T. (eds) Discovery Science. DS 2013. Lecture Notes in Computer Science(), vol 8140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40897-7_14

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  • DOI: https://doi.org/10.1007/978-3-642-40897-7_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40896-0

  • Online ISBN: 978-3-642-40897-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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