A form of statistical analysis used (in a form) in Paul Graham's initial "Plan for Spam" approach. Now used as a kind of catch-all term for this class of filters, no doubt horrifying statisticians everywhere.
Bayesian inference - Bayesian inference is a branch of statistical inference that permits the use of prior knowledge in assessing the probability of model parameters in the presence of new data. Bayesian inference has been termed 'subjective' inference because it allows a certain subjectivity in the selection of the prior distribution. The prior distribution can strongly affect the posterior (the results). We regard Bayesian inference as a useful tool for exploratory analysis of data and as a way to rigourously compare different sets of assumptions. However use of priors necessarily implies a greater responsibility of the researcher to assure that they are not introducing unintentional biases into their results through their priors. For this reason it is very important to test the sensitivity of your conclusions to different prior distributions.
a statistical method of combining the likelihood ratio with additional information to produce an overall estimate of the strength of a piece of evidence. Named after the Reverend Thomas Bayes (1702-1761).
Bayesian spam filtering is the process of using Bayesian statistical methods to classify documents into categories. Bayesian filtering gained attention when it was described in the paper A Plan for Spam by Paul Graham, and has become a popular mechanism to distinguish illegitimate spam email from legitimate "ham" email. From Wikipedia