The University of Texas at Austin
Department of Electrical and Computer
Engineering
EE351K Probability, Statistics, and Random Processes
Fall Semester 2010
Instructor: Prof. Haris Vikalo
- Email: hvikalo AT ece DOT utexas DOT edu
- Phone: (512) 232-7922
- Office: ACES 3.110
- Hours: Tue, Thu 11:00am-12:00pm
Teaching Assistant: Srinadh B
- Email: bsrinadh AT gmail DOT com
- Office: TBD
- Hours: TBD
Lectures:
- Time: Tue, Thu 3:30pm-5:00pm
- Place: ENS 127
Textbook: Introduction to Probability
(Bertsekas and Tsitsiklis), Athena Scientific, 2nd edition, 2002, ISBN 978-1-886529-23-6.
Grading:
- Homeworks: 15%
- Midterm exams: 45%
- Final exam: 35%
- Class participation: 5%
Homework policy: There will be roughly weekly homework assignments posted
on Blackboard. Homeworks are to be submitted at the beginning of the class when
they are due. You may discuss homework problems with other students, but must
submit your own independent solution. Late homework assignments will not be accepted.
- Prerequisites: EE313 Linear Systems and Signals with a grade of at least C.
- Course objectives:
This course is an introduction to probability, statistics and random processes for
engineers. Throughout the course we will describe various applications of these concepts
that electrical engineers might encounter, such as process control, system reliability,
modeling of queues in networks, as well as important noise models in linear systems used
in circuit and receiver design.
- Course outline:
- Probability models and axioms, conditional probability, Bayes' rule, independence and counting.
- Discrete and continuous random variables, expectations, conditioning and joint distributions.
- Derived distributions, covariance, conditional expectation.
- Inequalities, weak law of large numbers and central limit theorems and their applications.
- Bayesian and classical statistical inference: maximum a posteriori rule, least mean square
error estimation, linear least square estimation, parameter estimation, linear regression and
binary hypothesis testing.
- Bernoulli process and discrete time Markov chains.