Gaussian variational approximate inference for generalized linear mixed models

Publication Type:
Journal Article
Journal of Computational and Graphical Statistics, 2012, 21 (1), pp. 2 - 17
Issue Date:
Filename Description Size
Thumbnail2011005407OK.pdf330.8 kB
Adobe PDF
Full metadata record
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 (GLMMs) 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. Supplemental materials are available online. © 2012 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Please use this identifier to cite or link to this item: