Naive Bayes and Bayesian Networks apply Bayesian Decision Theory to data sets consisting of data points that are represented as strings of features that can be present or absent.
Within this framework, Naive Bayes, is the method that results from the assumption that the features are pairwise mutually stochastically independent. Bayesian Networks, on the other hand, result from making such an independence assumption only for some, but not for all, pairs of features.
Questions about stochastic dependence and independence arise frequently when developing a media monitoring solution such as the one we have developed here at PANOPTICOM. In this video, we give an overview over the topic based on the experience thus derived.