Events
Fall 2014

# Polynomial Solving Seminar

Thursday, November 6th, 2014, 11:00 am12:00 pm

The maximum likelihood degree (ML degree) of a statistical model is the number of complex solutions to the likelihood equations for generic data.  Drton, Sturmfels, and Sullivant have conjectured that the ML degree for Gaussian graphical models when the underlying graph is the $m$-cycle is $(m-3)2^{m-2}+1$. In this talk, we will introduce Gaussian graphical models, explore the ML degree when the underlying graph of the model is a cycle, and present the latest computational results obtained with PHCpack and Bertini.