Paul Bissex gives a ringing review to the Bayesian spam-fighter for OS X, SpamSieve:
That's the hot thing in spam fighting now — Bayesian filtering. I'll leave the details to smarter people, but it is essentially a statistical method in which individual tokens (words) are mapped to probabilities. For example, a quick look at my spam log of 700+ recent spams shows that my last name shows up in 4 spams and 254 "good" messages, making it a strong (but not absolute) indicator of non-spam. Conversely, the term "hcode" shows up in 304 spam messages and no legitimate messages, making it a very good indicator of spam. What's "hcode"? I have no idea — something that shows up in spammers' HTML a lot, I'd guess. It's obviously incredibly predictive, yet I never would have created a rule to look for it.
That's the beauty of this approach. Instead of trying to cleverly create individual rules that identify spam, you simply feed your Bayesian engine a pile of spam, and a pile of good mail, and it learns the difference. (It does weighting like SpamAssassin, but instead of weighting rules, it individually weights every unique word.) Read Paul Graham's highly influential "A Plan for Spam" essay for more on this. Really, read it. It's excellent.
