Towards Jointree Propagation with Conditional Probability Distributions

  • Cory J. Butz
  • Hong Yao
  • Howard J. Hamilton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)


In this paper, we suggest a novel approach to jointree computation. Unlike all previous jointree methods, we propose that jointree computation should use conditional probability distributions rather than potentials. One salient feature of this approach is that the exact form of the messages to be transmitted throughout the network can be identified a priori. Consequently, irrelevant messages can be ignored, while relevant messages can be computed more efficiently. We discuss four advantages of our jointree propagation method.


Bayesian Network Leaf Node Marginal Distribution Directed Acyclic Graph Chordal Graph 
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 2004

Authors and Affiliations

  • Cory J. Butz
    • 1
  • Hong Yao
    • 1
  • Howard J. Hamilton
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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