Belief Updating in Bayesian Networks

Part of the Information Science and Statistics book series (ISS)


In this chapter, we present algorithms for probability updating. An efficient updating algorithm is fundamental to the applicability of Bayesian networks. As shown in Chapter 2, access to P(\( \mathcal{U} \), 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 Time Slice Computation Tree Recursive Call Elimination Order 
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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© Springer Science +Business Media, LLC 2007

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