EEE 554 : Random Signal Theory
General Information
Instructor: Professor Bahman Moraffah
Office: GWC 333
Office Hours: TTh 10:30-11:30 am or by appointment
Class Meet: TTh 12:00-1:15 pm in SS105
Course Link: Piazza
Email: bahman.moraffah@asu.edu
Course Policies
Prerequisites:
EEE 350 Random Signal Analysis at ASU or an equivalent upper-division undergraduate course that covers probability and the fundamentals of one and multiple random variables
Undergraduate course in linear algebra
Thorough knowledge of one-variable and multi-variable calculus
Piazza: The main mode of electronic communication between students and staff, as well as amongst students, will be through https:piazza.comasufall2019/eee554. Piazza is intended for general questions about the course, clarifications about assignments, student questions to each other, discussions about material, and so on. We strongly encourage students to participate in discussion, ask and answer questions through this site.
Collaboration: You are encouraged to work on homework problems in study groups of no more than 3 people; however, you must always write up the solutions on your own, and you must never read or copy the solutions of other students. Similarly, you may use books or online resources to help solve homework problems, but you must always credit all such sources in your writeup and you must never copy material verbatim. Offering and accepting solutions from others is an act of plagiarism, which is a serious offense and all involved parties will be penalized according to the Academic Honesty Policy.
Quizzes and Exams: All quizzes and exams are closed book and notes, a single letter-sized notes sheet (front
and back) is allowed.
Scribe: We need volunteers to take notes each class, type it up and send it to me so it can be uploaded for the entire class. Each student can scribe at most 2 lectures. Scribing is NOT mandatory but it is highly encouraged. To get extra credit, you must take notes and type it in the provided tempalte. Extra points are at the instructor’s discretion and depends on student's effort. You can download the template here.
Syllabus
You can find the syllabus here
Review of probability basics– Axioms of probability, experiments, outcomes, events, conditional probability and independence
Random variables and their transformations– Continuous and discrete distribution and density functions (normal, uniform, binomial), conditional distributions, functions of one RV, mean, variance, moments, characteristic functions
Multivariate random variables– Joint distributions and density functions, marginal statistics, functions of two RVs, joint moments and characteristic functions, conditional distributions, multivariate normal distribution
Asymptotic behavior– Law of large number and central limit theorem
Stochastic processes– Properties, white noise, Gaussian random processes, stationary processes, power spectrum, systems with stochastic inputs, Kalman filter
Introduction to Markov chains
Parameter estimation– Maximum likelihood estimators and Bayesian estimators
Advanced topics– Concentration of measure and uniform bounds, Jackknife and Bootstrap
Textbook
Probability, Statistics, and Random Processes with Applications in Learning Theory, Bahman Moraffah, Book Draft (Will be distributed on Canvas)
Reference
Stochastic Processes: Theory for Applications, 1st Edition, Robert G. Gallager, Cambridge University Press, 2014. Available online here.
Intuitive Probability and Random Processes Using MATLAB, 1st Edition, Steven Kay, Springer, 2006. (Electronic version available, ASU library Resources: QA273 K326 2006eb Online)
Probability and Random Processes, 3rd Edition, Geoffrey R. Grimmett and Geoffrey R. Grimmett, Oxford University Press, 2001.
Probability and Stochastic Processes, 3rd Edition, Roy Yates, John Wiley and Sons, 2014.
Essentials of Stochastic Processes, 3rd Edition, Richard Durrett, Springer, 2016.
Probability, Random Variables, and Stochastic Processes, 4th Edition, Athanasios Papoulis
and S. Unnikrishna Pillai, McGraw-Hill, 2002.
Probability and Random Processes for Electrical and Computer Engineers, 1st Edition, John
A. Gubner , Cambridge University Press, 2006.
An Introduction to Statistical Signal Processing, 1st Edition, Robert M. Gray, 2014, Available
online here.
Introduction to Probability, 2nd Edition, Dimitri P. Bertsekas and John N. Tsitsiklis, Athena
Scientific, 2008. (A great introductory book for fundamentals of probability)
Exams
Midterm 1: 12:00-1:15 pm, September 26, 2019
Midterm 2: 12:00-1:15 pm, October 29, 2019
Final Exam: 12:10-2:00 pm, December 10, 2019,
Assessment
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