Pi Mu Epsilon talk
Title: An Introduction to Markov Chain Mixing Time
Speaker: Caprice Stanley, The Johns Hopkins University Applied Physics Laboratory
Date and time: Friday, April 15, 4-5 p.m.
Place: Duques Hall, Room 251
Abstract: Markov chains are useful tools which can be used to model a wide range of random processes. In this talk we will formally define Markov chains, explore examples and non-examples, and identify some desirable properties. The discussion will also focus on Markov chain mixing time and highlight a few interesting applications. In the first, we draw a connection to card shuffling and the famous result from Aldous and Diaconis which answers the question, ``How many shuffles are sufficient to mix a standard deck?'' In the second application, we discuss the use of Markov chain mixing time in work by Chikina, Frieze, and Pegden as it relates to the detection of gerrymandering in congressional district maps.
Fri, April 15, 2022
4:00 p.m. - 5:00 p.m.
Duques Hall, School of Business