Bayesian Filter
A spam filtering technique that uses probability and learning to identify spam based on email content.
Definition
A Bayesian filter is a spam detection method that uses statistical probability to classify emails as spam or legitimate. Named after Thomas Bayes, it learns from examples of spam and legitimate emails, calculating the probability that specific words or patterns indicate spam. The filter adapts over time as it processes more emails, becoming increasingly accurate for each user.
Why It Matters
Understanding Bayesian filtering helps you avoid spam triggers in your emails. These filters learn what looks like spam, so using certain words, formatting, or patterns can increase your spam score. Writing natural, relevant content helps you pass Bayesian analysis and reach the inbox.
How It Works
The filter maintains probability scores for words and phrases. When analyzing an email, it calculates the combined probability based on all content. Words like 'free', 'guarantee', or 'act now' might have high spam probability, while words specific to the recipient's interests have low probability. The combined score determines spam likelihood.
Best Practices
- 1Write natural, conversational email copy
- 2Avoid excessive use of spam-associated words
- 3Include relevant content that matches subscriber interests
- 4Maintain consistent branding and sending patterns
- 5Encourage recipients to mark your emails as 'not spam' if filtered incorrectly