What is the role of sufficient statistics in Naïve Bayes parameter learning?

Naïve Bayes relies on sufficient statistics like counts, means, and variances for efficient parameter estimation.

Sufficient statistics summarize all necessary information from data needed to estimate model parameters without storing raw data. In NB, class counts and feature-wise statistics fully define likelihood and prior distributions. This property enables fast training, distributed computation, and streaming updates.