Gaussian variational approximation with a factor covariance structure

TL;DR

This paper introduces a scalable Gaussian variational approximation method for high-dimensional Bayesian inference using a factor covariance structure and stochastic gradient optimization, demonstrated on real datasets.

Contribution

It proposes a novel, efficient approach for high-dimensional Gaussian variational approximation employing a factor covariance structure and reparametrization trick.

Findings

Effective in high-dimensional settings

Reduces computational burden compared to full covariance

Demonstrated on eight real datasets

Abstract

Variational approximation methods have proven to be useful for scaling Bayesian computations to large data sets and highly parametrized models. Applying variational methods involves solving an optimization problem, and recent research in this area has focused on stochastic gradient ascent methods as a general approach to implementation. Here variational approximation is considered for a posterior distribution in high dimensions using a Gaussian approximating family. Gaussian variational approximation with an unrestricted covariance matrix can be computationally burdensome in many problems because the number of elements in the covariance matrix increases quadratically with the dimension of the model parameter. To circumvent this problem, low-dimensional factor covariance structures are considered. General stochastic gradient approaches to efficiently perform the optimization are described, with gradient estimates obtained using the so-called "reparametrization trick". The end result is a flexible and efficient approach to high-dimensional Gaussian variational approximation, which we illustrate using eight real datasets.