Basic stochastic
processes, including Markov chains, Poisson processes, and birth
death processes. Topics from queues, renewal processes,
stationary processes, Brownian motion.
The course will trace the evolution of various areas of mathematics, such
as analysis, algebra, and geometry. While it will involve a great deal of
technical mathematics, the course will also explore issues closely bound up
with its progress, such as the changing standards of rigor in mathematics,
the cultural context of mathematics, the roles of problems and crises in
the development of mathematics, and the roles of intuition and logic in its
development.
This course is an introduction to stochastic, or random, processes.
Stochastic processes are models which represent phenomena that change
in a random way over time. Simple examples are (a) the amount of money
a gambler has after each play of a game and (b) the number of people
waiting for service at a bank at various times. This course studies
some of the most basic stochastic processes, including Markov chains and
Poisson processes. A Markov chain is a stochastic
process in which predictions for the future depend only on the present
state of affairs, but not on knowledge of the past behaviour of the process.
Markov chains are relatively easy to analyze, and they have been used as
models in many areas of science, management, and social science.
A Poisson process is a model for the occurrence of random events (such as
oil spills in the Atlantic Ocean). This course will treat both the theory
and applications of these stochastic processes.
Prerequisite:AS/SC/AK/ MATH 2030 3.0.
Coordinator: D. Salopek
