IC566: Random Signals and Random Processes (Spring 2019)
Instructor:
Kyung-Joon Park (kjp@, x6314, and E3-513)
Office hours: Right after class or by appointment
Class hour and classroom:
MW 13:30-15:00 pm, E3-113
Textbooks:
- Introduction to Probability, Dimitri P. Bertsekas and John N. Tsitsiklis, Athena Scientific, 2nd edition, 2008
Course Description:
This will be an introductory graduate level course on probability and random processes.
The classes will have three phases:
First, we will begin with the review of undergraduate level probability and random variables.
Second, we will proceed to cover the basic concepts and engineering examples of discrete and continuous random processes, such as Bernoulli, Poisson, and Markov.
Last, we will apply our understanding of probabilistic models to detection and estimation problems of random signals.
Grading policies:
Homework 30%, Midterm exam 30%, Final exam 40%
Announcement:
Lectures (tentative):
- [Feb. 25] Probability models and axioms
- [Feb. 27] Conditioning and Bayes rule
- [Mar. 4] Independence
- [Mar. 6] Counting
- [Mar. 11] Discrete random variables; probability mass functions; expectations
- [Mar. 13] Discrete random variable examples; joint PMFs
- [Mar. 18] Multiple discrete random variables: expectations, conditioning, independence
- [Mar. 20] Continuous random variables
- [Mar. 25] Multiple continuous random variables
- [Mar. 27] Continuous Bayes rule; derived distributions
- [Apr. 1] Derived distributions; convolution; covariance and correlation
- [Apr. 3] Iterated expectations; sum of a random number of random variables
- [Apr. 8] Bernoulli process
- [Apr. 10] Review
- [Apr. 15] Midterm exam
- [Apr. 17] No class (midterm week)
- [Apr. 22] Poisson process – I
- [Apr. 24] Poisson process – II
- [Apr. 29] Markov chains – I
- [May 1] Markov chains – II
- [May 6] No class (alternative holiday of 어린이날)
- [May 8] Markov chains – III
- [May 13] Weak law of large numbers
- [May 15] Central limit theorem
- [May 20] Bayesian statistical inference – I
- [May 22] No class (IEEE ICC 2019)
- [May 27] (Class begins at 1 pm) Bayesian statistical inference – II & Classical inference – I
- [May 29] (Class begins at 1 pm) Classical inference – I & Classical inference – II
- [June 3] Classical inference – II
- [June 5] Classical inference – III
- [June 10] Final exam