Random Copolymer: Gaussian Variational Approach

Random Copolymer: Gaussian Variational Approach

We study the phase transitions of a random copolymer chain with quenched disorder. We calculate the average over the quenched disorder in replica

Autoregressive Gaussian Random Vectors of First Order

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...

The Variational Gaussian Process

Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models. We develop the variational Gaussian process (VGP), a Bayesian nonparametric variational family, which adapts its shape to match complex posterior distributions. The VGP generates approximate posterior samples by generating latent inputs an...

Decoupled Variational Gaussian Inference

Variational Gaussian (VG) inference methods that optimize a lower bound to the marginal likelihood are a popular approach for Bayesian inference. A difficulty remains in computation of the lower bound when the latent dimensionality L is large. Even though the lower bound is concave for many models, its computation requires optimization over O(L) variational parameters. Efficient reparameterizat...