Loading...
Organizing Committee
Rice University
Brown University
Brown University
University of California, Berkeley
University of Texas at Austin
Brown University
Abstract
Data-rich investigations need advanced tools for allowing data to inform and interact with models. Bayesian Nonparametrics is a rapidly growing subfield of statistics and machine learning that provides a framework for creating complex statistical models that are both expressive and tractable. Recent, successful applications of nonparametric Bayesian models across a variety of domains suggests that these models have the potential for wide use. The challenge of constructing and using models on very high dimensional or even infinite dimensional spaces creates many opportunities for fruitful interactions between mathematicians, statisticians and computer scientists. Areas of interest include prior construction, posterior inference, posterior asymptotics, algorithmic development, and practical applications.
Image courtesy of Daniel Roy and Yee Whye Teh
Loading...
Loading participant list in background...
Tutorial Week Schedule
Thursday, September 13, 2012
9:00 - 10:30 AM EDT
Gaussian Processes I - Ryan Adams, Harvard University
Location: 11th Floor Lecture Hall
10:30 - 10:50 AM EDT
Coffee/Tea Break
Location: 11th Floor Collaborative Space
10:50 AM - 12:20 PM EDT
Gaussian Processes II - Ryan Adams, Harvard University
Location: 11th Floor Lecture Hall
12:20 - 1:40 PM EDT
Break for Lunch
1:40 - 3:10 PM EDT
Combinatorial Stochastic Processes I - Tamara Broderick, University of California, Berkeley
Location: 11th Floor Lecture Hall
3:10 - 3:30 PM EDT
Coffee/Tea Break
Location: 11th Floor Collaborative Space
3:30 - 5:00 PM EDT
Combinatorial Stochastic Processes II - Tamara Broderick, University of California, Berkeley
Location: 11th Floor Lecture Hall
Lecture Videos
Tamara Broderick
Massachusetts Institute of Technology
September 20, 2012 • 12:00 AM
