How does Naïve Bayes behave under Bayesian robustness frameworks?

Naïve Bayes can be made more robust using Bayesian priors, smoothing, and robust likelihood modeling.

Bayesian robustness involves reducing sensitivity to model misspecification. In NB, this is achieved through prior smoothing, robust distributions (e.g., heavy-tailed alternatives to Gaussian), and feature pruning. These techniques reduce the impact of outliers and distributional mismatches.