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December 2015 Variational Inference for Count Response Semiparametric Regression
J. Luts, M. P. Wand
Bayesian Anal. 10(4): 991-1023 (December 2015). DOI: 10.1214/14-BA932

Abstract

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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J. Luts. M. P. Wand. "Variational Inference for Count Response Semiparametric Regression." Bayesian Anal. 10 (4) 991 - 1023, December 2015. https://doi.org/10.1214/14-BA932

Information

Published: December 2015
First available in Project Euclid: 4 February 2015

zbMATH: 1335.62054
MathSciNet: MR3432247
Digital Object Identifier: 10.1214/14-BA932

Rights: Copyright © 2015 International Society for Bayesian Analysis

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Vol.10 • No. 4 • December 2015
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