How does Naïve Bayes compare with deep probabilistic models in uncertainty estimation?

Naïve Bayes provides simple uncertainty estimates, while deep probabilistic models capture richer uncertainty structures.

Naïve Bayes computes uncertainty using closed-form posterior probabilities but assumes independence and simple distributions. Deep probabilistic models like Bayesian neural networks or variational inference methods capture epistemic and aleatoric uncertainty more effectively, especially in complex data distributions.