Introduction
Bayes' theorem says posterior ∝ likelihood × prior, but the normalising integral is usually impossible to compute by hand. For special pairs of prior and likelihood — conjugate pairs — the posterior has the same form as the prior, and updating is just adjusting a few numbers. These cases build intuition for all of Bayesian ML and are used directly in A/B testing, bandits and Bayesian linear regression.