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Title: Flexible variational Bayes based on a copula of a mixture Authors:  David Gunawan - University of Wollongong (Australia) [presenting]
Robert Kohn - University of New South Wales (Australia)
David Nott - National University of Singapore (Singapore)
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, 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 application to Bayesian deep feedforward neural network regression models.