Bayesian Analysis

Variational Inference for Count Response Semiparametric Regression

J. Luts and M. P. Wand

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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, 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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Bayesian Anal., Volume 10, Number 4 (2015), 991-1023.

First available in Project Euclid: 4 February 2015

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approximate Bayesian inference generalized additive mixed models mean field variational Bayes penalized splines real-time semiparametric regression


Luts, J.; Wand, M. P. Variational Inference for Count Response Semiparametric Regression. Bayesian Anal. 10 (2015), no. 4, 991--1023. doi:10.1214/14-BA932.

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