Event Type:

Probability Seminar

Date/Time:

Tuesday, May 30, 2017 - 16:00 to 17:00

Location:

GILK 100

Abstract:

Mixing times measure how quickly a Markov process approaches its stationary distribution. Often computer scientists and statistical physicists use Markov chains to model networks with *n* nodes or physical systems with *n* particles and are interested in the mixing time as a function of large *n*. In this talk we explore a novel and general method of computing the mixing time asymptotics for one-dimensional diffusive Markov chains. The related question of which chains exhibit cutoff phenomena and with what window-size is also discussed.