AISTATS 2016poster75 citations
Variational Gaussian Copula Inference
Shaobo Han, Xuejun Liao, David Dunson, Lawrence Carin
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.
BibTeX
@InProceedings{pmlr-v51-han16,
title = {Variational Gaussian Copula Inference},
author = {Han, Shaobo and Liao, Xuejun and Dunson, David and Carin, Lawrence},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
pages = {829--838},
year = {2016},
editor = {Gretton, Arthur and Robert, Christian C.},
volume = {51},
series = {Proceedings of Machine Learning Research},
address = {Cadiz, Spain},
month = {09--11 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v51/han16.pdf},
url = {https://proceedings.mlr.press/v51/han16.html},
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.}
}