Abstract
Reasoning with incomplete and unreliable information is a central characteristic of decision making, for example in industry, medicine and finance. Bayesian networks provide a theoretical framework for dealing with this uncertainty using an underlying graphical structure and the probability calculus. Bayesian networks have been successfully implemented in areas as diverse as medical diagnosis and finance. We present a brief introduction to Bayesian networks for those readers new to them and give some pointers to the literature.
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© 2008 Springer-Verlag Berlin Heidelberg
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Holmes, D.E., Jain, L.C. (2008). Introduction to Bayesian Networks. 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_1
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DOI: https://doi.org/10.1007/978-3-540-85066-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85065-6
Online ISBN: 978-3-540-85066-3
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