Unlock: Bayesian Linear Regression

Gaussian prior, Gaussian likelihood, Gaussian posterior. Full posterior derivation by completing the square in the exponent: the posterior mean equals the ridge estimator, the predictive distribution has irreducible plus epistemic variance, and the marginal likelihood gives a closed-form hyperparameter selection criterion. Worked numeric example with three data points carries the algebra end to end.