  Maximum likelihood learning with exponential families leads to
moment-matching of the sufficient statistics, a classic result. This can be
generalized to conditional exponential families and/or when there are hidden
data. This document gives a first-principles explanation of these generalized
moment-matching conditions, along with a self-contained derivation.
