Abstract
In this chapter we introduce causal networks, which are the basic graphical feature for (almost) everything in this book. We give rules for reasoning about relevance in causal networks; is knowledge of A relevant for my belief about B? These sections deal with reasoning under uncertainty in general. Next, Bayesian networks are defined as causal networks with the strength of the causal links represented as conditional probabilities. Finally, the chain rule for Bayesian networks is presented. The chain rule is the property that makes Bayesian networks a very powerful tool for representing domains with inherent uncertainty. The sections on Bayesian networks assume knowledge of probability calculus as laid out in Sections 1.1–1.4.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2007 Springer Science +Business Media, LLC
About this chapter
Cite this chapter
(2007). Causal and 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_2
Download citation
DOI: https://doi.org/10.1007/978-0-387-68282-2_2
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-68281-5
Online ISBN: 978-0-387-68282-2
eBook Packages: Computer ScienceComputer Science (R0)