June 19, 2018

Fields Institute Graduate School Information Day
222 College Street, Toronto

Saturday, November 17, 2007

On the afternoon of November 17, The Fields Institute will host an information session for universities to display information on their graduate programs in mathematics, statistics and computer science. As part of the day's activities there will be two keynote lectures aimed at undergraduate students in the mathematical sciences. Please join us for this event and this opportunity to talk to representatives from the various university graduate programs.
All are welcome.
We are making a table (and poster board if requested) available to each university. Universities who wish to participate, and who have not already contacted The Fields Institute to confirm their participation should do so by sending an e-mail to the address listed below. Universities with several departments are asked to cooperate on using the space.
Fields can assist Universities with renting a van or bus to facilitate student travel for the afternoon, to request assistance please contact programs(PUT_AT_SIGN_HERE) Students traveling from outside of Toronto can request partial support of their travel expense, please retain original receipts and contact programs(PUT_AT_SIGN_HERE) to request support.


12:00 p.m.

Open time--University Information Sessions
Confirmed Participating Universities include:


Carleton - Mathematics & Statistics
Concordia - Mathematics & Statistics
McMaster - Mathematics & Statistics
Waterloo -Mathematics
- Mathematics, Statistics & Actuarial Sciences
Wilfrid Laurier
- Mathematics
Windsor - Mathematics and Statistics

Guelph - Mathematics & Statistics
Ontario Institute of Technology
- Mathematics &Computer Science
York -Mathematics & Computer Science

1:10 p.m.

Speaker: Jeffrey S. Rosenthal, Department of Statistics, University of Toronto

What is MCMC?
Why does the house always win at the casino? And, how do modern researchers compute high-dimensional integrals for Bayesian inference? These two questions are related. Markov chain Monte Carlo (MCMC) algorithms are very widely used in statistics, computer science, physics, and chemistry (800,000 Google hits!). They use repeated randomness to sample from complicated probability distributions and converge to the right answer, similar to how casinos' profits converge to infinity. I will explain the connections using simple graphical simulations, and will also explain how mathematical analysis can sometimes provide insights into the workings of these algorithms.

2:00 p.m. Reception and University Information Sessions

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