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Finding Most Likely Solutions

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Computation and Logic in the Real World (CiE 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4497))

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Abstract

As one simple type of statistical inference problems we consider Most Likely Solution problem, a task of finding a most likely solution (MLS in short) for a given problem instance under some given probability model. Although many MLS problems are NP-hard, we propose, for these problems, to study their average-case complexity under their assumed probabality models. We show three examples of MLS problems, and explain that “message passing algorithms” (e.g., belief propagation) work reasonably well for these problems. Some of the technical results of this paper are from the author’s recent joint work with his colleagues [WST, WY06, OW06] .

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Author information

Authors and Affiliations

  1. Dept. of Math. and Comput. Sci., Tokyo Inst. of Technology, Japan

    Osamu Watanabe

  2. Dept. of Computer Sci. and Eng., Chalmers Univ. of Technology, Sweden

    Mikael Onsjö

Authors
  1. Osamu Watanabe
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  2. Mikael Onsjö
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Editor information

Editors and Affiliations

  1. Dept. of Pure Mathematics, University of Leeds, LS2 9JT, Leeds, UK

    S. Barry Cooper

  2. Institue for Logic, Language and Computation (ILLC), Universiteit van Amsterdam, Plantage Muidergracht 24, 1018, TV Amsterdam, The Netherlands

    Benedikt Löwe

  3. Dipartimento di Scienze Matematiche ed Informatiche “R. Magari”, University of Siena, 53100, Siena, Italy

    Andrea Sorbi

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Watanabe, O., Onsjö, M. (2007). Finding Most Likely Solutions. In: Cooper, S.B., Löwe, B., Sorbi, A. (eds) Computation and Logic in the Real World. CiE 2007. Lecture Notes in Computer Science, vol 4497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73001-9_81

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  • DOI: https://doi.org/10.1007/978-3-540-73001-9_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73000-2

  • Online ISBN: 978-3-540-73001-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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