Gaussian Variational Approximate Inference for Generalized Linear Mixed Models

Authors

J. T. Ormerod, University of WollongongFollow
M. P. Wand, University of WollongongFollow

Publication Date

2009

Recommended Citation

Ormerod, J. T. and Wand, M. P., Gaussian Variational Approximate Inference for Generalized Linear Mixed Models, Centre for Statistical and Survey Methodology, University of Wollongong, Working Paper 04-09, 2009, 31p.
https://ro.uow.edu.au/cssmwp/24

Abstract

Variational approximation methods have become a mainstay of contemporary Machine Learning methodology, but currently have little presence in Statistics. We devise an effective variational approximation strategy for fitting generalized linear mixed models (GLMM) appropriate for grouped data. It involves Gaussian approximation to the distributions of random effects vectors, conditional on the responses. We show that Gaussian variational approximation is a relatively simple and natural alternative to Laplace approximation for fast, non-Monte Carlo, GLMM analysis. Numerical studies show Gaussian variational approximation to be very accurate in grouped data GLMM contexts. Finally, we point to some recent theory on consistency of Gaussian variational approximation in this context.