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Numerical Algebraic Geometry in Algebraic Statistics

Algebra & Discrete Mathematics

Speaker: Jose Rodriguez, UC Berkeley, Dept. of Mathematics
Location: 1147 MSB
Start time: Thu, Mar 21 2013, 3:10PM

Maximum likelihood estimation is a fundamental computational task in statistics and involves beautiful geometry. We discuss this task for determinantal varieties (matrices with rank constraints) and show how numerical algebraic geometry can be used to maximize the likelihood function. Our computational results with the software Bertini led to surprising conjectures and duality theorems. This is joint work with Jan Draisma, Jon Hauenstein, and Bernd Sturmfels.