Stochastic Processes

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 bank at various times. This course studies some of the most basic stochastic processes, including Markov chains, Poisson processes, and renewal 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). A renewal process is a process that ''starts over'' whenever a certain kind of random event occurs (such as a computer starting up after a crash). This course will treat both the theory and applications of these stochastic processes.

The text will probably be Introduction to Probability Models by Sheldon M. Ross (Academic Press).

The final grade is likely to be based on assignments (20%), a test (30%) and a final examination (50%).