Unlock: Maximum A Posteriori (MAP) Estimation

Maximum a posteriori estimation as the posterior mode of a Bayesian model: derivation, the flat-prior recovery of MLE, the worked L2-norm-equals-Gaussian-prior and L1-norm-equals-Laplace-prior equivalences that make ridge and lasso Bayesian, and the invariance failure under reparameterization that distinguishes MAP from MLE.