Abstract: 

We explain how the notion of message passing can be used to streamline the algebra and computer coding for fast approximate inference in large Bayesian statistical models. In particular, this approach is amenable to handling arbitrarily large models of particular types once a set of primitive operations is established.
The approach is founded upon a message passing formulation of mean field variational Bayes that utilizes factor graph representations of statistical models. The notion of factor graph fragments is introduced and is shown to facilitate compartmentalization of the required algebra and coding.

Speaker

Matt Wand

Research Area
Affiliation

University of Technology Sydney

Date

Fri, 26/05/2017 - 4:00pm

Venue

RC-M032, Red Centre, UNSW