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IFML Seminars
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GDC 6.302

COWS and Their Hybrids: Customized Orthogonal Weights

Larry Wasserman
University UPMC Professor of Statistics and Data Science, Carnegie Mellon University

Abstract: Particle physicists developed an algorithm called COWs (Customized Orthogonal Weights) for separating signals from backgrounds in certain experiments. We look at COWs from a statistical perspective. Then we consider several extensions of the method. In particular, a modified version of the method leads to a robust method for estimating arbitrary mixtures of conditionally independent distributions.

This is joint work with Chad Schafer and Mikael Kuusela.

Speaker Bio: Larry Wasserman is University UPMC Professor of Statistics and Data Science at Carnegie Mellon University. He is also Professor in the Machine Learning Department. He is a member of STAMPS (STAtistical Methods for the Physical Sciences).

Zoom link: https://utexas.zoom.us/j/84254847215