EE 322 (STAT 322): Probabilistic
Methods for Electrical Engineers
Fall 2012
The course will cover descriptions of discrete and
continuous random variables (probability mass function, cumulative distribution
function and probability density function); mean and variance computation;
conditioning and Bayes rule; statistical independence; and joint, conditional
and marginal pdf and cdf. Bernoulli, Binomial, Geometric, Poisson, Uniform,
Exponential, Gaussian and other distributions of interest to EE students will
be discussed. Moment generating functions, PMF, PDF of sums or random variables
will also be covered. Covariance, correlation and Bayesian least squares (and
linear least squares) estimation will be covered. Markov and Chebyshev
inequality, law of large numbers, central limit theorem.
Announcements
- Instructor: Prof.
Namrata Vaswani
- Office Hours: Mon-Wed 10-11am, Thurs
1-2pm
- Email: namrata AT
iastate.edu
- Office: 3121 Coover Hall
- Phone: 515-294-4012
- Teaching Assistant: Songtao Lu
- Email: songtao AT
iastate.edu
- Office:
- Webpage:
- Important Announcements
will be posted on class webpage.
- Blackboard:
- Blackboard will be used
for handouts and for homeworks/solutions and for grades
- Textbooks/References:
- Text: Bertsekas & Tsitiklis, Introduction
to Probability, Athena Scientific
- Other references:
- Yates and
Goodman, Probability and Stochastic Processes: A Friendly
Introduction for Electrical and Computer Engineers, John Wiley &
Sons, 1998.
- Cooper and McGillem, Probabilistic
Methods of Signal and System Analysis, Oxford, Third edition
- Ross, A First
Course in Probability, 6th ed. Prentice Hall, 2001
- Disability accommodation: If you have a
documented disability and anticipate needing accommodations in this
course, please make arrangements to meet with me soon. You will need to
provide documentation of your disability to Disability Resources (DR)
office, located on the main floor of the Student Services Building, Room
1076, 515-294-7220.
- Prerequisites: EE 224, Basic Calculus and Linear Alegbra.
- You should be familiar
with basic calculus, e.g. you should be able to sum and integrate common
sequences and functions, e.g., sum a geometric progression and integrate
constants, exponentials, and sinusoids. You should be familiar with
elementary linear algebra, e.g. understand vector and matrix notation and
be fluent with simple operations with matrices and vectors. You should
also be familiar with the ideas of an inverse of a matrix and the
determinant of a matrix.