Course Title:
Applied Probability and Stochastic Processes
Course Description:
Covers fundamentals of probability and stochastic processes with applications to estimation and queuing theory. Includes basic laws of probability, conditioning, and Bayes rule. Topics include random variables and their functions; PDF, PMF, and CDF notions; statistical averages; moments and characteristic functions; multiple random variables; joint and conditional PDF and PMF; multiple functions of random variables; correlation and covariance; mean squared estimation of random variables; Markov, Chebychev, and Chernov inequalities; various notions of convergence of random variable sequences; laws of large numbers; central limit theorem; and large deviation theory. As time permits, discusses basic notions of estimation and properties of estimators, unbiased and minimum variance estimation, CRLB, sufficient statistics, consistency of estimators, basic notions of discrete and continuous-time random processes, mean and autocorrelation function, WSS and cyclo-stationary processes, ergodicity of random processes, and other topics.
Fall Offering:
Lab/Coreq 1:
Spring Offering:
Lab/Coreq 2:
Summer Offering:
Lab/Coreq Remarks:
Summer 1 Offering:
Prerequisite 1:
Summer 2 Offering:
Prerequisite 2:
Cross-Listed Course 1:
Prerequisite 3:
Cross-Listed Course 2:
Prerequisite 4:
Cross-Listed Course 3:
Prerequisite 5:
Cross-Listed Course 4:
Prerequisite Remarks:
Strong understanding of linear systems, transform techniques, and linear algebra.
Cross-Listed Course 5:
Repeatable:
N
