EC EN
670
Stochastic Processes
Hours
3.0 Credit, 3 Lecture, 0 Lab
Semester
Fall
Review of elementary probability, introduction to random processes: definitions, properties, covariance, spectral density, time average, stationarity, ergodicity, linear system relations, mean square estimation, Markov processes.
1.
Understand the foundation of stochastic processes including: elementary probability, definitions, properties, expectation, covariance and correlation, stationarity, ergodicity, power spectral density, and application to linear systems.
2.
Apply mathematical analysis to engineering examples of Gaussian, Poisson, and Markov processes.
3.
Simulate, compare, and evaluate the use of random processes in written communication to a technical audience.