EE 497B - Probability and Random Processes for Electrical Engineers
Credits and Contact Hours: 3 credits; two 75-minute lectures and one 50-minute recitation
every week
Course Instructor: George Kesidis
University Bulletin Description: E E 497B (3) Probability elective for EE/CMPEN students
with applications in signal processing, communications, networking, control systems, remote
sensing, power systems and circuit analysis.
Prerequisite: EE 350 or EE 353
Prerequisites by Topics:
1. An understanding of continuous-time system theory, and in particular, convolution,
Fourier transforms, and linear filters.
2. Proficiency in the use software for numerical computation and graphing.
Designation: Mathematical statistics elective for electrical engineering majors
Course Outcomes:
After successfully completing this course, students will be able to:
1. Characterize probability models using probability mass (density) functions and
cumulative distribution functions.
2. Characterize pairs of random variables using joint probability mass (density) functions,
marginal probability mass (density) functions, and joint cumulative distribution
functions.
3. Characterize functions of random variables.
4. Calculate and understand the significance of the expected value, variance, and standard
deviation of a random variable.
5. Describe conditional and independent events and conditional random variables.
6. Characterize the sum of independent random variables using the moment generating
function and the Central Limit Theorem.
7. Analyze the performance of signal detection systems using binary hypothesis testing.
8. Characterize statistical confidence in decision making with uncertainty.
9. Determine the autocorrelation and spectral density of stationary random processes.
10. Characterize the response of LTI systems driven by a stationary random process using
autocorrelation and power spectral density functions.
11. Utilize software to study and apply probability theory through simulation of experiments
with random outcomes
Course Topics:
1. Experiments, models and axioms
2. Discrete random variables
3. Basic concepts of information theory
4. Continuous random variables
5.
6.
7.
8.
Pairs of random variables
Random vectors
Signal detection using binary hypothesis testing
Sums of random variables: laws of large numbers, central limit theorem, statistical
confidence
9. Wide-sense stationary stochastic processes
10. Continuous-time linear filtering of WSS processes
11. Estimation and the Wiener-Hopf Filter
Student Outcomes Addressed:
O.4.1. Graduates will have an in-depth technical knowledge in one or more areas of
specialization.
O.4.2. Graduates will have a practical understanding of the major electrical engineering
concepts and demonstrate application of their theoretical knowledge of the concepts.