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A '''Bayesian network''', '''Bayes network''', '''belief network''', '''decision network''', '''Bayes(ian) model''' or '''probabilistic directed acyclic graphical model''' is a probabilistic [[graphical model]] (a type of [[statistical model]]) that represents a set of variables and their [[conditional independence|conditional dependencies]] via a [[directed acyclic graph]] (DAG). Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor.  For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

A '''Bayesian network''', '''Bayes network''', '''belief network''', '''decision network''', '''Bayes(ian) model''' or '''probabilistic directed acyclic graphical model''' is a probabilistic [[graphical model]] (a type of [[statistical model]]) that represents a set of variables and their [[conditional independence|conditional dependencies]] via a [[directed acyclic graph]] (DAG). Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor.  For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.