EE 640
Applied Random Processes
Fall 2016
Outline
Course
Instructor: Tony Kuh, Office: 205E POST
Office Hours: MW 10:3011:50 or
by appointment
Phone Number: 9567527
Email: kuh@hawaii.edu
Prerequisites
 Math 471 or EE342, (Probability Theory)
 Linear Time Invariant Systems
 Fourier Transforms, Laplace Transforms
Grading (Approximate)
 HW: (quizzes) 20 (assignments)
 MT: 30 (exam directory)
 Final: 50 (exam directory)
Course Description (lecture summary)
Probability and random variables:

Probability triple, sigma fields, probability axioms, independence,
random variables, multivariate distributions, expectation,
functions of random variables, Gaussian random variables.
Convergence of random sequences:

Stochastic convergence, Laws of Large Numbers, Central Limit Theorem.
Introduction to Estimation:

Bayesian estimation, minimum mean squared error estimation, conditional
expectation, projection theorem.
Random processes:

Stationarity, ergodicity, Gaussian random processes, Markov processes,
Poisson process
Second Order Processes

Autocorrelation function,
power Spectral
density, linear time invariant systems, Karhunen Loeve Expansion,
baseband and narrowband processes.
Applications

Linear minimum mean
square estimation, Wiener filters, Kalman filters, Hypothesis
testing, Hidden Markov models, Expectation Maximization algorithm.
References

B. Hajek, Random
Processes for Engineers, Cambridge University Press, 1st. Ed., 2015 (required).

G. Grimmett and D. Stirzaker, Probability and Random
Processes, Oxford University Press, 3rd. Ed., 2001 (optional).

R. G. Gallager, Stochastic Processes: Theory for Applications,
Cambridge University Press, 1st. Ed., 2014.

H. Stark and J. W. Woods, Probability, Statistics, and Random Processes
for Engineers, Prentice Hall, 4th Ed., 2011.
