Georgia Institute of Technology College of Sciences

Maximum likelihood estimation is a fundamental computational task in statistics and it also involves some beautiful mathematics. The MLE problem can be formulated as a system of polynomial equations whose number of solutions depends on data and the statistical model. For generic choices of data, the number of solutions is the ML-degree of the statistical model. But for data with zeros, the number of solutions can be different. In this talk we will introduce the MLE problem, give examples, and show how our work has applications with ML-duality.This is a current research project with Elizabeth Gross.