Fast Approximate Inference for Arbitrarily Large Statistical Models via Message Passing
- Date: Wednesday 25 July 2018, 11:00 – 12:00
- Location: Mathematics Level 8, MALL 1, School of Mathematics
- Type: Seminars, Statistics
- Cost: Free
Professor Matt Wand. University of Technology Sydney, Australia. Part of the statistics seminar series.
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.