Teaching Staff
In many real-life processes, there is uncertainty so that deterministic
models fail to represent the actual systems. Such processes are known as
stochastic processes or random processes. In order to build realistic models,
stochastic models have been developed to capture uncertainty in systems. At the
beginning of this course we will study various regression models. We will focus
on the problems of estimation and statistical inference when developing
regression models. The regression models developed will be helpful for
understanding the relationships among variables, prediction and forecasting, and
decision making. Then, we will cover several popular and well-studied stochastic
processes: Markov chains, the Poisson process and continuous-time Markov chains.
Throughout the course, we will use real-world examples to illustrate the theories and
demonstrate the applications.
Students should have workable knowledge in basic
probability and statistics, at the level of
SEEM 2430 / ENGG 2430
or equivalent.
Please submit your assignment to Assignment Box: D07, 5/F, ERB.
Late assignment submissions will incur a deduction of 25
marks per day. However, no assignment will be accepted after the solution is
posted.
Last Updated: April 25, 2015