research
∙
06/05/2023
Variational inference based on a subclass of closed skew normals
Gaussian distributions are widely used in Bayesian variational inference...
share
research
∙
10/19/2022
Second order stochastic gradient update for Cholesky factor in Gaussian variational approximation from Stein's Lemma
In stochastic variational inference, use of the reparametrization trick ...
research
∙
09/01/2021
Explicit natural gradient updates for Cholesky factor in Gaussian variational approximation
Stochastic gradient methods have enabled variational inference for high-...
research
∙
04/21/2019
Conditionally structured variational Gaussian approximation with importance weights
We develop flexible methods of deriving variational inference for models...
research
∙
11/10/2018
Bayesian variational inference for exponential random graph models
Bayesian inference for exponential random graphs (ERGMs) is a doubly int...
research
∙
05/18/2018
Model reparametrization for improving variational inference
In this article, we propose a strategy to improve variational Bayes infe...
research
∙
12/24/2017
Efficient data augmentation techniques for Gaussian state space models
We propose a data augmentation scheme for improving the rate of converge...
research
∙
05/18/2016
Gaussian variational approximation with sparse precision matrices
We consider the problem of learning a Gaussian variational approximation...
research
∙
02/25/2015
