Fall 2013-2014
Instructor: Billur Barshan
Office: EE-404
Office Hour: Wednesday: 14:00-15:30
Tel: 290-2161 or x2161
Teaching Assistant: office: EE-, tel: , e-mail:

Prerequisite:
An introductory course on probability theory (e.g., MATH 255)

Credit Units: 3

Course Outline:

1. Review of probability theory
2. Random sequences and convergence
3. Random (stochastic) processes
   • Basics (autocorrelation, autocovariance, stationarity, ergodicity)
   • Stochastic calculus (continuity, differentiability, integrability)
   • Poisson process and its derivatives
   • White-noise process (continuous, discrete)
   • Gaussian (normal) process
   • Random walk, Brownian motion, Wiener process
   • Markov chains
   • Markov processes
   • Linear systems driven by random inputs
   • Shot-noise process

Grading:
Midterm 1 (25%): date to be arranged, open book, in class
Midterm 2 (25%): date to be arranged, open book, in class
Final (35%): to be arranged
Homework (15%)

Textbook:
Although there is no ideal textbook for the course, any of the following should be helpful:

1. Stochastic Processes, S. M. Ross, John Wiley, 1983.
2. Random Signals, Detection, Estimation and Data Analysis, K. S. Shanmugan, A. M. Breipohl, John Wiley, 1988.
3. Probability and Random Processes for Electrical Engineering, A. Leon-Garcia, 2nd edition, 1994.
4. Probability, Random Variables, and Stochastic Processes, A. Papoulis, 3rd edition, McGraw-Hill, 1991.
5. Introduction to Random Processes with Applications to Signals and Systems, W. A. Gardner, Macmillan, 1986.
6. Probability, Random Processes, and Estimation Theory for Engineers, H. Stark, J. W. Woods, Prentice Hall, 1986.
7. Probability, Random Variables and Random Signal Principles, P. Z. Peebles, McGraw-Hill, 1993.
8. Stochastic Processes, J. L. Doob, John Wiley, 1990.
9. Probability and Random Processes, Problems and Solutions, G. R. Grimmet, D. R. Stirzaker, Oxford Science Publications, 1991.
   

Link to EE Department's web page for this course.