Automating variational inference for statistics and data mining

Invited talk by Tom Minka, Machine Learning and Perception Group, Microsoft Research, Cambridge, UK

Chair: Jonathan Templin, Tuesday 21st July, 14.30 - 15.15, Uppercroft, School of Pythagoras.

Tom MinkaI will describe Infer.NET, a free software package from Microsoft that automatically applies variational Bayesian inference to a statistical model of your choosing.  Unlike sampling methods, variational methods approximate the posterior distribution as a point estimate plus uncertainty, making them well suited to large-scale time-varying datasets.  Infer.NET is structured as a compiler: it takes a model specification as input and produces a specialized inference program as output.  This automated process makes it easy to experiment with different models and get an efficient program for each one.  I will demonstrate Infer.NET with models from the psychometric literature.