Abstract

Bayesian modeling, where the full posterior distribution rather than just a point estimate is typically the object of interest, continues to see unprecedented challenges as well as rapid developments driven by methodological, computational and theoretical innovations, aided by recent phenomenal advances in computing power. The purpose of this session is to bring together four confirmed and distinguished speakers at the forefront of the development of Bayesian methodology for complex models. They are: Daniel Kowal (Rice University, Statistics), Donatello Telesca (UCLA, Biostatistics), Sylvia Fruehwirth-Schnatter (WU Wien, Applied Statistics and Econometrics) and Anindya Bhadra (Purdue University, Statistics). The focus of the session is intentionally broad, with the common theme being the speakers are leaders in Bayesian methodology, in domains such as mixture models, massive data, functional data, graphical models and high-dimensional data. Similarly, the application domains of the speakers are also broad: ranging from econometrics, to genomics, to spatial and environmental statistics. Careful attention has been paid to achieve a diversity on geographical locations, seniority levels, home departments and backgrounds of the speakers.