Abstract
Bayesian Networks are an important area of research and application within the domain of Artificial Intelligence. This paper explores the nature and implications for Bay esian Networks beginning with an overview and comparison of inferential statistics with Bayes’ Theorem. The nature, relevance and applicability of Bayesian Network theory for issues of advanced computability form the core of the current discussion. A number of current applications using Bayesian networks are examined. The paper concludes with a brief discussion of the appropriateness and limitations of Bayesian Networks for human-computer interaction and automated learning.
This paper is revised from an earlier work dated December 1, 1998, ©1998, 2008 by Daryle Niedermayer. All Rights Reserved.
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© 2008 Springer-Verlag Berlin Heidelberg
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Niedermayer, D. (2008). An Introduction to Bayesian Networks and Their Contemporary Applications. In: Holmes, D.E., Jain, L.C. (eds) Innovations in Bayesian Networks. Studies in Computational Intelligence, vol 156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85066-3_5
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DOI: https://doi.org/10.1007/978-3-540-85066-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85065-6
Online ISBN: 978-3-540-85066-3
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