Probabilistic networks are graphical models of (causal) interactions among a set of variables, where the variables are represented as vertices (nodes) of a graph and the interactions (direct dependences) as directed edges (links or arcs) between the vertices. Any pair of unconnected vertices of such a graph indicates (conditional) independence between the variables represented by these vertices under particular circumstances that can easily be read from the graph. Hence, probabilistic networks capture a set of (conditional) dependence and independence properties associated with the variables represented in the network.
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
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
(2008). Networks. In: Bayesian Networks and Influence Diagrams. Information Science and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-74101-7_2
Download citation
DOI: https://doi.org/10.1007/978-0-387-74101-7_2
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-74100-0
Online ISBN: 978-0-387-74101-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)