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Transcript
DEPARTMENT OF ELECTRICAL AND
COMPUTER ENGINEERING
Course
ECE 30200 - Probabilistic Methods in Electrical Engineering
Type of Course
Required for the CmpE and EE programs
Catalog Description
An introductory treatment of probability theory including distribution
and density functions, moments, and random variables. Applications
of normal and exponential distributions. Estimation of means,
variances, correlation, and spectral density functions. Random
processes and response of linear systems to random inputs.
Credits
3
Contact Hours
3
Prerequisite Courses
MA 36300
Corequisite Courses
ECE 30100
Textbook
R. D. Yates and D. J. Goodman, Probability and Stochastic Systems: A
Friendly Introduction for Electrical and Computer Engineers, Wiley,
Current Edition.
Course Objectives
This course is designed to serve as an introduction to experiments,
models and probabilities. The following topics will be covered:
probability models defined on abstract sets; individual discrete and
continuous random variables; pairs of random variables including
joint probability functions, conditional probability functions,
correlation, and covariance; a brief introduction to stochastic
processes with an emphasis on the Poisson process.
Course Outcomes
Students who successfully complete this course will have
demonstrated:
1. An understanding of the basic concepts of probability models
defined on abstract sets. (a, e)
2. An understanding of the three axioms of probability. (a, e)
3. An understanding of the law of total probability, Bayes’
theorem, and independence. (a, e)
4. An understanding of counting methods and application to
independent trials. (a, e)
Department Syllabus
ECE – 30200
Page | 1
5. An understanding of probability mass functions and probability
density functions. (a, e)
6. An understanding of derived random variables and random
variables conditioned on events. (a, e)
7. An understanding of pairs of random variables, including joint
probability functions, conditional probability functions,
correlation, and covariance. (a, e)
8. An understanding of the basic concepts of stochastic processes.
(a, e)
9. An understanding of the Poisson process and its properties. (a,
e)
Lecture Topics
1. Experiments, models, and probability
2. Discrete random variables
3. Continuous random variables
4. Pairs of random variables
5. Stochastic processes
Computer Usage
Low
Laboratory Experience
None
Design Experience
None
Coordinator
Chao Chen, Ph.D.
Date
31/3/11
Department Syllabus
ECE – 30200
Page | 2