Aimed at: Masters students

Background: Linear algebra. 

Some experience with statistics and probability is preferable, but not necessary. Some experience with rings and ideals is preferable, but not necessary.

Algebraic statistics uses methods from algebraic geometry, commutative algebra, and geometric combinatorics for making statistical inferences. In this seminar some of the basics in the field of algebraic statistics will be discussed, with emphasis on topics covered in the book "Lectures on Algebraic Statistics" by  Drton, Sturmfels and Sullivan. If necessary, we will also cover some basic algebraic geometry. A temporary webpage can be found at http://home.mathematik.uni-freiburg.de/shanekelly/algstat2016-2017winter.html

Course text: Lectures on Algebraic Statistics by Mathias Drton, Bernd Sturmfels and Seth Sullivant.

Recommended additional reading:

1. S. Lauritzen: Graphical Models, Oxford University Press, New York, 1996

2. D. A. Cox, J. Little, and D. O'Shea: Ideals, Varieties, and Algorithms: An introduction to computational algebraic geometry and commutative algebra, Springer, 2007

3. Sturmfels: Solving Systems of Polynomial Equations, American Mathematical Society, 2002

4. Piotr Zwiernik: Semialgebraic Statistics and Latent Tree Models, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, 2015