Formally, Bayesian networks are directed acyclic graphs whose nodes represent random variabless in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses.
Formally, Bayesian networks are directed acyclic graphs whose nodes represent variables, and whose edges encode conditional independencies between the variables. Nodes represent random variables, but in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses.