
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 email 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)fields.utoronto.ca
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)fields.utoronto.ca
to request support.
TENTATIVE SCHEDULE FOR THE DAY
12:00 p.m.
Noon

Open timeUniversity Information
Sessions
Confirmed Participating Universities include:


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

Guelph  Mathematics
& Statistics
Ontario Institute of Technology
UToronto  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 highdimensional 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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