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Comparing Hierarchical Markov Networks and Multiply Sectioned Bayesian Networks

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Foundations of Intelligent Systems (ISMIS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2871))

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Abstract

Multiply sectioned Bayesian networks (MSBNs) were originally proposed as a modular representation of uncertain knowledge by sectioning a large Bayesian network (BN) into smaller units. More recently, hierarchical Markov networks (HMNs) were developed in part as an hierarchical representation of the flat BN.

In this paper, we compare the MSBN and HMN representations. The MSBN representation does not specify how to section a BN, nor is it a faithful representation of BNs. On the contrary, a given BN has a unique HMN representation, which encodes precisely those independencies encoded in the BN. More importantly, we show that failure to encode known independencies can lead to unnecessary computation in the MSBN representation. These results, in particular, suggest that HMNs may be a more natural representation of BNs than MSBNs.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada, S4S 0A2

    C. J. Butz & H. Geng

Authors
  1. C. J. Butz
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  2. H. Geng
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Editor information

Editors and Affiliations

  1. The International WIC Institute, Beijing University of Technology, China

    Ning Zhong

  2. Department of Computer Science, University of North Carolina, NC 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Shimane University, 89-1 Enya-cho Izumo, 6938501, Shimane, Japan

    Shusaku Tsumoto

  4. Department of Informatics, Graduate School of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi, 819-0395, Fukuoka, Japan

    Einoshin Suzuki

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© 2003 Springer-Verlag Berlin Heidelberg

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Butz, C.J., Geng, H. (2003). Comparing Hierarchical Markov Networks and Multiply Sectioned Bayesian Networks. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_77

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  • DOI: https://doi.org/10.1007/978-3-540-39592-8_77

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20256-1

  • Online ISBN: 978-3-540-39592-8

  • eBook Packages: Springer Book Archive

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