Bayesian Networks

Bayesian networks extend Naive Bayes by representing complex probabilistic relationships between variables using directed acyclic graphs (DAGs). They explicitly model conditional dependencies.

Key characteristics include capturing causal relationships and handling missing data. Imagine a doctor mapping how smoking increases lung cancer risk and leads to shortness of breath.