Скачать или смотреть Flexible Variational Bayes based on a Copula of a Mixture of Normals

About the talk:
Variational Bayes methods approximate the posterior density by a family of tractable distributions and use optimisation to estimate the unknown parameters of the approximation. Variational approximation is useful when exact inference is intractable or very costly. Our article develops a flexible variational approximation 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 normals variational approximations.

About the speaker:
Robert Kohn is Scientia Professor at the University of New South Wales. Robert’s research and teaching interests include Bayesian methodology, variable selection and model averaging, nonparametric regression models, time series modelling, multivariate Gaussian and non-Gaussian regression; and Markov chain Monte Carlo simulation algorithms.