Variational inference for count response semiparametric regression BY J. LUTS AND M.P. WAND

Fast variational approximate algorithms are developed for Bayesian semiparametric regression when the response variable is a count, i.e. a non-negative integer. We treat both the Poisson and Negative Binomial families as models for the response variable. Our approach utilizes recently developed methodology known as non-conjugate variational message passing. For concreteness, we focus on generalized additive mixed models, al-though our variational approximation approach extends to a wide class of semiparametric regression models such as those containing interactions and elaborate random effect struc-ture