Implicit Copula Variational Inference

Variational Gaussian Copula Inference

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 nonpa...

Copula variational inference

We develop a general variational inference method that preserves dependency among the latent variables. Our method uses copulas to augment the families of distributions used inmean-field and structured approximations. Copulas model the dependency that is not captured by the original variational distribution, and thus the augmented variational family guarantees better approximations to the poste...

Variational inference with copula augmentation

Kernel Implicit Variational Inference

Recent progress in variational inference has paid much attention to the flexibility of variational posteriors. Work has been done to use implicit distributions, i.e., distributions without tractable likelihoods as the variational posterior. However, existing methods on implicit posteriors still face challenges of noisy estimation and can hardly scale to high-dimensional latent variable models. ...

Copula Variational Bayes inference via information geometry

Variational Bayes (VB), also known as independent mean-field approximation, has become a popular method for Bayesian network inference in recent years. Its application is vast, e.g. in neural network, compressed sensing, clustering, etc. to name just a few. In this paper, the independence constraint in VB will be relaxed to a conditional constraint class, called copula in statistics. Since a jo...