Variational Gaussian copula inference
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, Conference
Han, S; Liao, X; Dunson, DB; Carin, L
Published in: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016
January 1, 2016
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.
Duke Scholars
Published In
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016
Publication Date
January 1, 2016
Start / End Page
829 / 838
Citation
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MLA
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Han, S., Liao, X., Dunson, D. B., & Carin, L. (2016). Variational Gaussian copula inference. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016 (pp. 829–838).
Han, S., X. Liao, D. B. Dunson, and L. Carin. “Variational Gaussian copula inference.” In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, 829–38, 2016.
Han S, Liao X, Dunson DB, Carin L. Variational Gaussian copula inference. In: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016. 2016. p. 829–38.
Han, S., et al. “Variational Gaussian copula inference.” Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, 2016, pp. 829–38.
Han S, Liao X, Dunson DB, Carin L. Variational Gaussian copula inference. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016. 2016. p. 829–838.
Published In
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016
Publication Date
January 1, 2016
Start / End Page
829 / 838