Stochastic Gradient News Today

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...

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.