Efficient variational inference for generalized linear mixed models with large datasets
Abstract
The article develops a hybrid Variational Bayes algorithm that combines the mean-field and fixed-form Variational Bayes methods. The new estimation algorithm can be used to approximate any posterior without relying on conjugate priors. We propose a divide and recombine strategy for the analysis of large datasets, which partitions a large dataset into smaller pieces and then combines the variational distributions that have been learnt in parallel on each separate piece using the hybrid Variational Bayes algorithm. The proposed method is applied to fitting generalized linear mixed models. The computational efficiency of the parallel and hybrid Variational Bayes algorithm is demonstrated on several simulated and real datasets.
Cite
@article{arxiv.1307.7963,
title = {Efficient variational inference for generalized linear mixed models with large datasets},
author = {David J Nott and Minh-Ngoc Tran and Anthony Y. C. Kuk and Robert Kohn},
journal= {arXiv preprint arXiv:1307.7963},
year = {2013}
}
Comments
23 pages, 4 figures