Variational Gaussian Copula Inference
Machine Learning
2016-05-19 v3 Machine Learning
Computation
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.
Cite
@article{arxiv.1506.05860,
title = {Variational Gaussian Copula Inference},
author = {Shaobo Han and Xuejun Liao and David B. Dunson and Lawrence Carin},
journal= {arXiv preprint arXiv:1506.05860},
year = {2016}
}
Comments
Appearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. JMLR: W&CP volume 51