Prof. John Paisley: September 18th

Title: Variational Inference and Big Data

Abstract:  I will discuss a scalable algorithm for approximating posterior distributions called stochastic variational inference. Stochastic variational inference lets one apply complex Bayesian models to massive data sets. This technique applies to a large class of probabilistic models and outperforms traditional batch variational inference, which can only handle small data sets. Stochastic inference is a simple modification to the batch approach, so a significant part of the discussion will focus on reviewing this traditional batch inference method.