Variational Gaussian Copula Inference
Variational Gaussian Copula Inference
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DOI：
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发表时间：
2015-06
影响因子：
2.2
通讯作者：
Shaobo Han;X. Liao;D. Dunson;L. Carin
中科院分区：
文献类型：
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作者：
Shaobo Han;X. Liao;D. Dunson;L. Carin
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.
