EE
322 (STAT 322): Probabilistic Methods for Electrical Engineers
Spring 2011
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. Time permitting, we
will also introduce basic concepts of (a) Monte
Carlo and importance sampling and (b) Markov chains.
- Announcements
- Exam 2
(take-home) has been posted in WebCT, will also be sent via email. I am
happy to give printouts to anyone who would prefer that.
- Exam 2
- Friday
April 22 2011
- Based on
Chapters 1, 2, 3, 4.1 (of second edition) or equivalently
Chapters 1, 2, 3 (of first edition) or Homeworks 1-10.
- Quiz 5
- on Friday
April 1
- Chapter 3
-- single random variable PDF and CDF; exponential and Gaussian
distrubutions
- MIDTERM
EXAM Wednesday March 9
- Chapters 1
and 2, HWs 1--6
- 4 pages of
notes' sheet (cheat sheet) allowed
- Quiz 4,
Friday, March 4
- Chapter 2,
mainly HW 5 and 6, i.e. joint and conditional PMF, independence,
conditional expectation
- Quiz 3 on
Monday, February 21
- PMF of
function of a single random variable
- Expectation,
Variance, Expectation of functions of a random variable
- Quiz 2 on
Friday, February 4
- Entire
Chapter 1 (exclude conditional independence).
- Focus on
independence, reliability examples and on conditional probability
- Homework 3
posted, due Friday Feb 4
- HW 2 deadline
postponed to Monday Jan 31
- HELP SESSION
/ RECITATION LOCATION CHANGED -- SEE BELOW
- Quiz on
Friday January 21
- Quiz will
be mostly based on material in last two-three lectures before the quiz.
In general all quizzes will be comprehensive (in this class everything
builds on the past, so it all has to be comprehensive).
- Same policy
for all quizzes.
- Midterm exam
dates:
- Midterm 1:
March 9, 2011 (Wednesday)
- Midterm 2: April 22, 2011 (Friday)
- Class time and Location: M-W-F
12:10 - 1 pm, Location:Physics 0003
- Help
Session/Recitation:
- Wednesdays 3-4pm in 1219
Coover
- Fridays
8-9am in 1011
Coover
- Class Information Sheet and
Syllabus
- Instructor: Prof. Namrata Vaswani
- Office Hours:
Mon-Tues-Wed 4-5pm
- Email: namrata AT
iastate.edu
- Office: 3121 Coover Hall
- Phone: 515-294-4012
- Teaching
Assistants: Kai Zhu and Teng Zhao
- Email: kzhu AT
iastate.edu , tzhao AT iastate.edu
- Office: 3209 Coover
Hall (Kai)
- Webpage:
- Important Announcements will be
posted on class webpage.
- WebCT:
- WebCT 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.