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Variational methods are attractive for computing Bayesian inference for highly parametrized models and large datasets where exact inference is impractical.
A stochastic approximation method
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Nonlinear statistical learning with truncated Gaussian graphical models
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Doubly stochastic variational Bayes for non-conjugate inference
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The multivariate skew-normal distribution
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An Introduction to Copulas (Springer Series in Statistics)
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Parametric probability densities and distribution functions for Tukey g-and-h transformations and their use for fitting data
Headrick, T. C., Kowalchuk, R. K., and Sheng, Y. (2008) · 2008
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Pair-copula constructions of multiple dependence
Aas, K., Czado, C., Frigessi, A., and Bakken, H. (2009) · 2009
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Variational boosting: Iteratively refining posterior approximations
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Variational inference: A review for statisticians
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Hosmer, D. W., Lemeshow, S., and Sturdivant, R. X. (2013) · 2013
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Inversion copulas from nonlinear state space models with an application to inflation forecasting
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Inference via low-dimensional couplings
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Xu, M., Quiroz, M., Kohn, R., and Sisson, S. A. (2018) · 2018
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Maximum likelihood estimation of skew-t copulas with its applications to stock returns
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