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"Flexible Variational Bayes Based on a Copula of a Mixture" by David Gunawan, Robert Kohn et al.

Variational Bayes methods approximate the posterior density by a family of tractable distributions whose parameters are estimated by optimization. Variational approximation is useful when exact inference is intractable or very costly. Our article develops a flexible variational approximation based on a copula of a mixture, which is implemented by combining boosting, natural gradient, and a variance reduction method. The efficacy of the approach is illustrated by using simulated and real datasets to approximate multimodal, skewed and heavy-tailed posterior distributions, including an application to Bayesian deep feedforward neural network regression models. Supplementary materials, including appendices and computer code for this article, are available online. ....

Natural Gradient , On Gaussian Posterior , Stochastic Gradient , Ariance Reduction ,

Forging New Connections Within IDEAL

The Institute for Data, Econometrics, Algorithms, and Learning hosted a day-long meeting June 7 to review progress and look ahead to summer and fall activities. ....

United States , Illinois Institute Of Technology , University Of Chicago , Pawan Poojary , Ravi Kannan , Natasha Devroye , Vaidehi Srinivas , Reza Gheissari , Adrienne Barris , Alekh Agarwal , Aravindan Vijayaraghavan , Randall Berry , Lev Reyzin , Avrim Blum , Samir Khuller , Liren Shan , Tim Tsz , Johna Dever , Northwestern Simpson Querrey Biomedical Research Center , Institute For Data , Google Research , Ready For Research , Indian Institute Of Science , University Of Illinois Chicago , Northwestern Weinberg College Of Arts , Toyota Technological Institute At Chicago ,