  This article describes posterior maximization for topic models, identifying
computational and conceptual gains from inference under a non-standard
parametrization. We then show that fitted parameters can be used as the basis
for a novel approach to marginal likelihood estimation, via block-diagonal
approximation to the information matrix,that facilitates choosing the number of
latent topics. This likelihood-based model selection is complemented with a
goodness-of-fit analysis built around estimated residual dispersion. Examples
are provided to illustrate model selection as well as to compare our estimation
against standard alternative techniques.
