Dr. Paul Marriott

Department of Statistics and Actuarial Science

University of Waterloo

* Title: *"Local and
Global Mixture Geometry"

* Abstract:*

This talk looks at some geometric ideas which have a found a rich application in statistical

inference. In general the inference problem with mixture models can be very challenging and

very different from the gold-standard of inference in exponential families. However a simplifying

modelling assumption, which often holds in practical situations, exploits the underlying geometry

of the mixture model and ensures that the resulting inference problem is tractable. This

simplification gives rise to a class of models which I have called local mixtures. The talk

describes these local mixture models and then investigates if the idea of local mixtures can

be extended to much more general families.