11:30 a.m. - 12:30 p.m. Location: SLC 2.303
Please note the change in the event time and location.
NSF Postdoctoral Fellow, Applied and Computational Mathematics and Statistics, University of Notre Dame
Visiting Scientist, Simons Institute for the Theory of Computing
Numerical Algebraic Geometry for Maximum Likelihood Estimation
Numerical algebraic geometry is a growing area of algebraic geometry that involves describing solutions of systems of polynomial equations. This area has already had an impact in kinematics, statistics, PDE's, and pure math. This talk will focus on using numerical algebraic geometry for maximum likelihood estimation in algebraic statistics.
The first half will be an introduction to solving polynomial equations. In the second half, computational results of Maximum Likelihood degrees (ML-degrees) for matrices with rank constraints, which are related to mixture models arising in statistics, will be presented. This is followed by a surprising theoretical result termed maximum likelihood duality.
The talk will conclude with remarks on future directions of research.
Prior to the colloquium, coffee will be served in the alcove outside of FO 2.406
Sponsored by the Department of Mathematical Sciences
John Zweck, 972-883-6699
Questions? Email me.