Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
Applied Nonparametric Bayes
Michael Jordan, UC Berkeley
October 18, 2004, 4:15PM
Bayesian approaches to learning problems have many virtues,
including their ability to make use of prior knowledge and
their ability to link related sources of information, but
they also have many vices, notably the strong parametric
assumptions that are often invoked in practical Bayesian
modeling. Nonparametric Bayesian methods offer a way to make
use of the Bayesian calculus without the parametric handcuffs.
In this talk I describe several recent explorations in nonparametric
Bayesian modeling and inference, including various versions
of ``Chinese restaurant process priors'' that allow flexible
structures to be learned and allow sharing of statistical
strength among sets of related structures. I discuss applications
to problems in bioinformatics and information retrieval.
Joint work with Yee Whye Teh and David Blei.
Slides are available here.
About the Speaker
Michael Jordan is Professor in the Department of Electrical Engineering
and Computer Science and the Department of Statistics at the University
of California at Berkeley. He received his Masters from Arizona State
University, and earned his PhD from the University of California, San
Diego. He was a professor at the Massachusetts Institute of Technology
from 1988 to 1998. His research in recent years has focused on
probabilistic graphical models, on kernel machines, and on applications
of statistical machine learning to problems in bioinformatics,
information retrieval, and signal processing.
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