What is Bayesian filtering?

Bayesian filtering uses probability mathematics to classify emails as spam or legitimate. The system learns from examples: given a corpus of known spam and known good email, it calculates the probability that any given word or phrase appears in spam versus legitimate messages. When evaluating new email, it combines these probabilities across all words to estimate overall spam likelihood.

The elegance of Bayesian filtering is adaptive learning. When users mark messages as spam or not-spam, the system updates its probability tables. This means filters evolve with changing spam tactics-new spam patterns get learned, and legitimate phrases that spammers co-opt get recalibrated. The filter becomes personalized to each user's mail patterns over time, which is why email you mark as spam influences future filtering.

Bayesian analysis is one component of modern filtering, not the entire system. Contemporary spam filters combine Bayesian scores with reputation signals, authentication verification, link analysis, and behavioral patterns in ensemble models. Understanding Bayesian filtering helps explain why unusual word combinations, excessive repetition, or statistically improbable language patterns trigger spam flags-you're failing a probability test trained on millions of spam examples.