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Published in:
Introduction to Computational Intelligence Read chapter Read first chapter

2016 | OriginalPaper | Chapter

26. Belief Revision

Authors : Rudolf Kruse, Christian Borgelt, Christian Braune, Sanaz Mostaghim, Matthias Steinbrecher

Published in: Computational Intelligence

Publisher: Springer London

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Abstract

In Chap. 23 we discussed graphical models as tools for structuring uncertain knowledge about high-dimensional domains. More precisely, we introduced Bayes networks, which are based on directed graphs and conditional distributions, and Markov networks, which refer to undirected graphs and marginal distributions or factor potentials. Further, we presented an evidence propagation method in Chap. 24 with which it is possible to condition the probability distribution represented by a graphical model on given evidence, i.e., on observed values for some of the variables, a reasoning process that is also called focusing.

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previous chapter Learning Graphical Models
next chapter Decision Graphs
Literature
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Metadata
Title
Belief Revision
Authors
Rudolf Kruse
Christian Borgelt
Christian Braune
Sanaz Mostaghim
Matthias Steinbrecher
Copyright Year
2016
Publisher
Springer London
Book
Computational Intelligence

Print ISBN: 978-1-4471-7294-9

Electronic ISBN: 978-1-4471-7296-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-1-4471-7296-3_26

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