Introduction
Maximum likelihood trusts the data completely, which goes wrong when data are scarce or models are flexible. Maximum a posteriori (MAP) estimation adds what we believed before seeing data — a prior — and picks the most probable parameters after combining both. It turns out that the familiar trick of regularization is exactly MAP estimation.