"Everything should be made as simple as possible, but not simpler." - Albert Einstein

Seminar Announcement

CNS SOARS Seminar - Marginal Inference for Hierarchical Models with Intractable Likelihoods

Ephraim Hanks, Department of Statistics, Colorado State University

Wednesday, February 23, 2011

11:00 a.m. - 12:00 p.m., Statistics Bldg, room 006

ABSTRACT

In applied settings, complex constraints and non-invertible transformations of variables can lead to hierarchical models in which it is impossible to write a likelihood model in closed form.   We develop an approach for making marginal inference for such models in the case when sampling from a distribution related to the intractable likelihood is possible.  This approach uses the weighted bootstrap within a data augmentation setting, and allows for straightforward inference based on multiple imputation.  We apply this approach to a study of landscape-scale gene flow in mule deer in Colorado and Utah.

BYODrink, Sandwiches provided at 10:45am, before start of talk.