Event Detail

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.