Title: Eigenvalues for stochastic matrices with a prescribed stationary distribution
Speaker: |
Steve Kirkland |
Affiliation: |
University of Manitoba |
Location: |
Please contact Sabrina Lato for Zoom link |
Abstract: A square nonnegative matrix T is called stochastic if all of its row sums are equal to 1. Under mild conditions, it turns out that there is a positive row vector w^T (called the stationary distribution for T) whose entries sum to 1 such that the powers of T converge to the outer product of w^T with the all-ones vector. Further, the nature of that convergence is governed by the eigenvalues of T.
In this talk we explore how the stationary distribution for a stochastic matrix exerts an influence on the corresponding eigenvalues.