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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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(2017). Bayesian Network. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_927
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_927
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