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Finding Most Likely Solutions

  • Osamu Watanabe
  • Mikael Onsjö
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4497)

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] .

Keywords

Parity Check Code Word Parity Check Matrix Optimal Assignment 2CNF Formula 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Osamu Watanabe
    • 1
  • Mikael Onsjö
    • 2
  1. 1.Dept. of Math. and Comput. Sci., Tokyo Inst. of TechnologyJapan
  2. 2.Dept. of Computer Sci. and Eng., Chalmers Univ. of TechnologySweden

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