### Maximum Likelihood Estimation for Data with Zeros

- Series
- Algebra Seminar
- Time
- Wednesday, August 21, 2013 - 15:05 for 1 hour (actually 50 minutes)
- Location
- Skiles 005
- Speaker
- Jose Rodriguez – UC Berkeley

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.