Bayesian networks represent an alternative approach to network modeling that requires edges to be directed. Formally, a Bayesian network is a directed acyclic graph (DAG) whose vertices are random variables X1, … , Xn that are probabilistic, can be discrete or continuous, and describe variation across conditions. In this context, each variable has a conditional distribution given its parents P(Xi|Parents(Xi)) and is independent of its nondescendants given its parents. Consequently, Bayesian networks allow only dependencies between a node and its parents, and conditional independence statements
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