Hypothesis testing, model selection, model comparison, model criticism, model multiplicity. These are all topics pertaining the uncertainty in the choice of a statistical model and its use for inferential purposes. Research on these areas span many decades. But a fully satisfactory answer to these issues has been elusive. This conference will present a comprehensive coverage of modern developments in the areas of model comparison, model averaging and multiplicity adjustments.

The main lecturer for the conference will be Jim Berger, who is The Arts and Sciences Professor of Statistics at the Department of Statistical Science, Duke University. It is hard to find someone better suited to present a clear articulation of the field of model uncertainty than Jim Berger. He is one of the most influential researchers in the area with lifelong work on the topic. Professor Berger will be joined by Susie Bayarri (University of Valencia, Spain) in the delivery of ten two-hour lectures. In addition to the lectures, two invited speakers will deliver complementary two-hour lectures.  These are Brad Efron (Stanford University) and Marc Suchard (University of California Los Angeles).  The conference will be hosted by the Department of Applied Mathematics and Statistics of the Baskin School of Engineering. The local organizers are Bruno Sansó, Abel Rodriguez and Yuefeng Wu.  



We acknowledge the kind support of the National Science Foundation (NSF), the Conference Board for the Mathematical Sciences (CBMS), the International Society for Bayesian Analysis (ISBA), and the Center for Information Technology Research in the Interest of Society (CITRIS).