Conditioning Graphs: Practical Structures for Inference in Bayesian Networks

  • Kevin Grant
  • Michael C. Horsch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)


Programmers employing inference in Bayesian networks typically rely on the inclusion of the model as well as an inference engine into their application. Sophisticated inference engines require non-trivial amounts of space and are also difficult to implement. This limits their use in some applications that would otherwise benefit from probabilistic inference. This paper presents a system that minimizes the space requirement of the model. The inference engine is sufficiently simple as to avoid space-limitation and be easily implemented in almost any environment. We show a fast, compact indexing structure that is linear in the size of the network. The additional space required to compute over the model is linear in the number of variables in the network.


Bayesian Network Leaf Node Internal Node Inference Engine Space Requirement 
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 2005

Authors and Affiliations

  • Kevin Grant
    • 1
  • Michael C. Horsch
    • 1
  1. 1.Dept. of Computer ScienceUniversity of SaskatchewanSaskatoon

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