
ECE 8702: Statistical Signal Processing


Course Description
This is an advanced course on probability, random signals, and stochastic process,
and will cover
Probability and Random Variables: discrete and continuous random variables, functions and
transformations of random variables, and conditional and joint distributions.
Random Processes: correlation and spectral analysis, the Gaussian process
and the response of linear systems to random processes.
Important Random Processes: Poisson process, Markov chain, random walk,
Wiener process, and Brownian motion.
Statistics: mean square estimation, parameter estimation, Baysian estimation.
Prerequisite: An introductory course on Probability, Random Variables, and Stochastic Processes is required.
Learning objectives
Students are exposed to the following topics:
Basic Probability: Events and Axioms
Discrete Random Variables
Continuous Random Variables Examples of continuous distributions
Central Limit Theorem
Estimation
Course Information
See the information in the links on the left.
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