IC666: Discrete Stochastic Processes (Spring 2012)
Instructor:
Kyung-Joon Park
Office hours: By appointment, 3-309 DGIST
Textbooks:
- Introduction to Probability, Dimitri P. Bertsekas and John N. Tsitsiklis, Athena Scientific, 2nd edition, 2008
- Discrete Stochastic Processes, Robert G. Gallager, Springer, 1995. (Draft of the second edition, "Stochastic Processes: Theory for Applications," freely available at http://www.rle.mit.edu/rgallager/notes.htm)
Course Description:
The purpose of this course is to build a conceptual understanding of tractable classes of models on discrete stochastic processes.
We study applications of probabilistic models in various areas. We also cover the essentials of the undergraudate-level probability course in order to gain a concrete understanding of basic probability models.
Grading policies:
Exam 70%, Homework 30%
Announcement:
Lectures:
- [March 6] Introduction to probabilistic systems
- [March 8] Conditional probability, Bayes' rule, total probability theorem
- [March 13] Independence
- [March 15] Counting
- [March 20] Discrete random variable
- [March 22] No class
- [March 27] Conditional expectation
- [March 29] Multiple discrete random variables
- [April 3, 5] No class
- [April 10] Continuous random variables I
- [April 12] Continuous random variables II
- [April 17] Continuous random variables, derived distributions (regular & possible makeup class)
- [April 19] Continuous random variables, derived distributions (cont'd) (regular & possible makeup class)
- [April 24] Midterm exam
- [April 26] No class (midterm exam week)
- [May 1] Transforms
- [May 3] Iterated expectations
- [May 8] Sum of a random number of random variables
- [May 10] No class
- [May 15] Weak law of large numbers
- [May 17] Bernoulli process
- [May 22] Poisson process
- [May 24] No class (ISET 2012)
- [May 29] Poisson process: Examples
- [June 1] Poisson process: Examples (cont'd)
- [June 5] More about Poisson process
- [June 7] Problems on Poisson distribution
- [June 15] Final exam