Naive Bayes and Bayesian networks are both probabilistic classifiers but differ in their assumptions of independence between predictor variables. Bayesian networks are more flexible as they can represent dependencies between variables through a directed acyclic graph structure. To develop a Bayesian network model, one identifies variables of interest, determines conditional dependencies between variables, quantifies these dependencies with probabilities, and represents them in a network diagram.

1. Discussion (Chapter 5): What is the relationship between Naïve
Bayes and Bayesian networks? What is the process of
developing a Bayesian networks model?
Note:
Response should be 250-300 words. There must be at least one
APA formatted reference (and APA in-text citation) to support
the thoughts in the post.