- General Description: This course consists of an
introduction to probability theory and its applications. The main goal
is to develop the basic mathematical tools to consider models that
incorporate uncertainty using a probabilistic framework. We start by
introducing the axioms of probability and the rules needed to perform
calculations with probabilities. We then move into the concepts of
independence, conditional probability and Bayes theory, define a random
variable, both discrete and continuous, and consider its probability
distribution function as well as its expectation and higher order
moments. We extend these ideas to the multivariate case. We consider
some more advanced topics like the Law of Large Numbers and Central
Limit theorem. We also consider applications to some simple
stochastic processes like Markov Chains and Poisson Processes.
- Textbook:
- Probability and Statistics (third edition). M.H. DeGroot
and M.J. Schervish. Addison Wesley
- Other References:
- Probability and Stochastic Processes: A Friendly
Introduction for Electrical and Computer Engineers. R.D. Yates and D.J.
Goodman. Wiley.
Grading: The course work
will be weigthed as follows: Quizes 30%, Midterm 35%, Final 35%.
There will be one midterm (35%,
10/24/02) and one final (35%, W 12/04/02, 12-3 PM).
Quizes
There will be five quizes that
will be held in class on: 10/01/02 ; 10/10/02 ; 11/05/02 ;
11/14/02 ; 11/26/02
There will be no make-up for
quizes, your lowest score will be dropped to allow for ANY
event that might prevent you from taking a particular quiz.
- Homeworks: There will be several (possibly weekly)
homeworks which will not be graded. Homeworks will give a very close
indication of the material that will be covered in exams and quizes.
- Sections: The TA will be in charge of the sections
on Tuesdays and Thurdays. You are asked to sign up for only one of the
two.
It is important that you check the web page frequently for
homeworks, solutions and announcements.