This is supplemental course information, designed to give you a fuller picture of the course and an expanded look at the topics covered. This is an unofficial document. The USC Course Catalog is the binding description of all university courses. Information such as books, materials covered, and the order of topics is subject to change. Please consult instructor for this semseter to get more upto date course information.
Catalog Data:
464 Probability Theory for Engineers (3, FaSpSm) Axiomatic foundations of probability, random variables, Gaussian and Poisson distributions, functions of a random variable. Gaussian random vector, functions of several random variables; sequences of random variables. Prerequisite: EE 301a (Introduction to Linear Systems) and MATH 445 (Mathematics of Physics and Engineering II).
Text book:
Leon-Garcia, A., Probability and Random Processes for Electrical Engineering, Second Edition, Addison-Wesley, 1994.
Course Coordinator:
Bart Kosko, Department of Electrical Engineering-Systems
Topics:
Algebra of events – set theory and logic
Probability as a measure in sample space
Conditional probability and independence
Bayes relations
Combinatorics
Poisson law
Probability densities and cumulative distributions
Random variables
Expectations and moments of random variable
Poisson, Bernoulli, Markov processes
Conditional expectations
Gaussian random vectors
Functions of random variables
Stochastic convergence
Laws of large numbers
Moment transforms
Limit theorems
Estimation
Introduction to statistics
Course Objectives:
This course covers the basic principles of probability theory. The approach is axiomatic with an emphasis on reasoning with probabilistic uncertainty. Includes applications to many engineering problems.
Course Outcomes:
The students will be able to:
1. Understand set theory as a description of random events.
2. Understand formal reasoning with logic, sets, and probabilistic concepts
3. Analyze the probabilistic structure of complex problems
4. Analyze systems in terms of random variables and moments.
5. Apply the basic principles of probability to problems in engineering, networks, signal processing, communications, and economics.
6. Have the background needed for the study of random processes and statistics.
7. Apply basic estimation and statistical principles to engineering problems.
Prepared by: Bart Kosko Date: June 14, 2004
