Variational inference for count response semiparametric regression

Publication Type:
Journal Article
Bayesian Analysis, 2015, 10 (4), pp. 991 - 1023
Issue Date:
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© 2015 International Society for Bayesian Analysis. Fast variational approximate algorithms are developed for Bayesian semiparametric regression when the response variable is a count, i.e., a nonnegative 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, although our variational approximation approach extends to a wide class of semiparametric regression models such as those containing interactions and elaborate random effect structure.
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