Belief Updating in Bayesian Networks

  • Finn V. Jensen
Part of the Statistics for Engineering and Information Science book series (ISS)


In this chapter, we present an algorithm for probability updating. An efficient updating algorithm is fundamental to the applicability of Bayesian networks. As shown in Chapter 1, access to P(U, e) is sufficient for the calculations. However, because the joint probability table increases exponentially with the number of variables, we look for more efficient methods. Unfortunately, no method guarantees a tractable calculation task. However, the method presented here represents a substantial improvement, and it is among the most efficient methods known.


Bayesian Network Undirected Graph Time Slice Elimination Order Junction Tree 
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 Science+Business Media New York 2001

Authors and Affiliations

  • Finn V. Jensen
    • 1
  1. 1.Department of Computer SciencesAalborg UniversityAalborg ØDenmark

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