Sonja Petrovic : An invitation to algebraic statistics

Posted by , part of the Statistics and Data Science Seminar.

At

Feb. 25, 2009, 4:15 p.m.

In

SEO 612

Abstract

Algebraic statistics is a maturing discipline whose main focus is the study of statistical models using the tools from algebraic geometry and computational algebra. The main concept is that statistical models are algebraic varieties. Algebraic approach can be used to provide Markov bases for the models, or to compute the maximum likelihood degree. Some of the best studied models so far are contingency tables, conditional independence models and graphical models including latent class. Algebraic statistics has also found applications in computational biology and phylogenetics. Some recent work shows that it can be used as a powerful tool for phylogenetic tree reconstruction and for model identifiability problems. This will be an introductory talk to explain some of the main concepts of the field, illustrated on a few examples.