B1581
Title: Flexible variational Bayes-based on a copula of a mixture
Authors: Robert Kohn - University of New South Wales (Australia) [presenting]
David Gunawan - University of Wollongong (Australia)
David Nott - National University of Singapore (Singapore)
Abstract: Variational Bayes methods approximate the posterior density by a family of tractable distributions whose parameters are estimated by optimisation. Variational approximation is useful when exact inference is intractable or very costly. The focus is on developing a flexible variational approximation based on a mixture copula, which is implemented by combining boosting, 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. Supplementary materials, including appendices and computer code for this article, are available online.
