Novel Algebraic Approaches to Maximum Likelihood Estimation

Novel Algebraic Approaches to Maximum Likelihood Estimation

The seventh “One World webinar” organized by YoungStatS will take place on November 17th, 2021. Maximum likelihood estimation (MLE) is a tool in data analysis to estimate a probability distribution or density in a statistical model for given data. In recent decades, algebraic and combinatorial tools have proved useful for computing MLEs and understanding the geometry of the MLE problem which in recent years led to new and interesting results in combinatorics and algebraic geometry. Selected young researchers active in the area of algebraic statistics will present their recent contributions on this topic.

When & Where:

Wednesday, November 17th, 18:00 CEST

Online, via Zoom. The registration form is available here.

Speakers:

• Jane Ivy Coons, University of Oxford, United Kingdom

• Aida Maraj, University of Michigan – Ann Arbor, USA

• Anna Seigal, Harvard University, USA

Discussant:

• Dr. Carlos Améndola, Technical University of Munich, Germany

The webinar is part of YoungStatS project of the Young Statisticians Europe initiative (FENStatS) supported by the Bernoulli Society for Mathematical Statistics and Probability and the Institute of Mathematical Statistics (IMS).