2011 George Bekey Lecture
Department of Computer Science

"Completely Random Measures for Bayesian Nonparametrics"

Professor Michael I. Jordan
University of California, Berkeley
 

Abstract
Computer Science has historically been strong on data structures and weak on inference from data, whereas Statistics has historically been weak on data structures and strong on inference from data. One way to draw on the strengths of both disciplines is to pursue the study on "inferential methods for data structures," i.e. methods that update probability distributions on recursively-defined objects such as trees, graphs, grammars, and function calls. This is accommodated in the world of "Bayesian on parametrics," where prior and posterior distributions are allowed to be general stochastic processes, and these tend to have interesting connections to combinatorics. Professor Jordan will focus on Bayesian on parametric modeling based on completely random measures, giving examples of how recursions based on these measures lead to useful models in several applied problem domains, including protein structural modeling, natural language processing, computational vision, and statistical genetics.

Biography
Michael I. Jordan is the Pehong Chen Distringuished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research in recent years has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in signal processing, statistical genetics, computational biology, information retrieval, and natural language processing. Professor Jordan was named to the National Academy of Sciences in 2010 and the National Academy of Engineering in 2010. He is a Fellow of the American Association for the Advancement of Science, the IMS, the ACM, and the IEEE.