Abstract
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.
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© 2007 Springer Science +Business Media, LLC
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(2007). Belief Updating in Bayesian Networks. In: Bayesian Networks and Decision Graphs. Information Science and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68282-2_4
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DOI: https://doi.org/10.1007/978-0-387-68282-2_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-68281-5
Online ISBN: 978-0-387-68282-2
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