Algebraic methods for maximum likelihood estimation

Algebra Seminar
Monday, April 9, 2018 - 3:05pm for 1 hour (actually 50 minutes)
Skiles 005 or 006
Kaie Kubjas – MIT / Aalto University –
Anton Leykin
Given data and a statistical model, the maximum likelihood estimate is the point of the statistical model that maximizes the probability of observing the data. In this talk, I will address three different approaches to maximum likelihood estimation using algebraic methods. These three approaches use boundary stratification of the statistical model, numerical algebraic geometry and the EM fixed point ideal. This talk is based on joint work with Allman, Cervantes, Evans, Ho┼čten, Kosta, Lemke, Rhodes, Robeva, Sturmfels, and Zwiernik.