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A Bayesian network is a form of directed graphical model for representing multivariate probability distributions.
The nodes of the network represent a set of random variables, and the directed arcs represent causal relationships between variables. The Markov property is usually required: every direct dependency between a possible cause and a possible effect has to be shown with an arc. Bayesian networks with the Markov property are called I-maps (independence maps). If all arcs in the network correspond to a direct dependence on the system being modeled, then the network is said to be a D-map (dependence-map). Each node is associated with a conditional probability distribution, that quantifies the effects the parents of the node, if any, have on it. Bayesian support various forms of reasoning: diagnosis, to derive causes from symptoms, prediction, to derive effects from causes, and intercausal reasoning, to discover the mutual causes of a common effect.
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© 2011 Springer Science+Business Media, LLC
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(2011). Bayesian Network. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_65
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DOI: https://doi.org/10.1007/978-0-387-30164-8_65
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8
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