Towards a Faster Inference Algorithm in Multiply Sectioned Bayesian Networks

  • Karen H. Jin
  • Dan Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)


Multiply sectioned Bayesian network(MSBN) is an extension of Bayesian network(BN) model for the support of flexible modelling in large and complex problem domains. However, current MSBN inference methods involve extensive intra-subnet(internal) and inter-subnet (external) message passings. In this paper, we present a new MSBN message passing scheme which substantially reduces the total number of message passings. By saving on both internal and external messages, our method improves the overall efficiency of MSBN inference compared with existing methods.


Bayesian Network Local Propagation Compilation Time Incoming Message Local Consistency 
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 2008

Authors and Affiliations

  • Karen H. Jin
    • 1
  • Dan Wu
    • 1
  1. 1.School of Computer ScienceUniversity of WindsorWindsorCanada

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