Variational Gaussian copula inference

Shaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models. For models with continuous and non-Gaussian hidden variables, we propose a semiparametric and automated variational Gaussian copula approach, in which the parametric Gaussian copula family is able to preserve multivariate posterior dependence, and the nonparametric transformations based on Bernstein polynomials provide ample flexibility in characterizing the univariate marginal posteriors.
Original languageEnglish (US)
Title of host publicationProceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016
PublisherPMLR
Pages829-838
Number of pages10
StatePublished - Jan 1 2016
Externally publishedYes

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