A1224
Title: Flexible variational Bayes based on a copula of a mixture of normals
Authors: Robert Kohn - University of New South Wales (Australia) [presenting]
David Nott - National University of Singapore (Singapore)
David Gunawan - University of Wollongong (Australia)
Abstract: Variational Bayes methods approximate the posterior density by a family of tractable distributions and use optimisation to estimate the unknown parameters of the approximation. The variational approximation is useful when exact inference is intractable or very costly. A flexible variational approximation is developed based on a copula of a mixture of normals, which is implemented using the natural gradient and a variance reduction method. The efficacy of the approach is illustrated by using simulated and real datasets to approximate multimodal, skewed and heavy-tailed posterior distributions, including an application to Bayesian deep feedforward neural network regression models. Each example shows that the proposed variational approximation is much more accurate than the corresponding Gaussian copula and a mixture of normal variational approximations.
