bayesian networks from first principles — the chain rule, conditional probability tables, d-separation and its valve intuition, markov blankets, i-maps, naive bayes classifiers and gaussian networks.
Conditional-Independence
2026-08-31
naive bayes is the classifier you get by taking bayes’ rule seriously and probability theory not seriously at all. 𐃏 it assumes every feature is independent of every other feature given the class — an assumption that is false for essentially all real data — and yet it filters spam, routes support tickets and triages documents well enough that it has survived five decades of fancier competition. this page derives it, counts why the “naive” part is the whole point, builds a spam filter from scratch, and is honest about where it breaks (its probabilities, not its decisions).