Probability and Random Signals. (3-0) 3 Credit Hours

Catalog description

Prerequisite: EE 3423 or equivalent. Probability axioms, conditional probability, Bayes’ theorem, and independence. Probability models for a single discrete or continuous random variable: cumulative distribution function (CDF), probability mass function (PMF), probability density function (PDF), expected value, variance, and standard deviation. Specific families of random variables, such as Bernoulli, geometric, binomial, uniform, exponential, and Gaussian random variables. Models for multiple random variables: joint CDF, joint PMF, and joint PDF; marginal PMF and marginal PDF; random variable independence, covariance, and correlation. Theorems pertaining to sequences of random variables, such as the Central Limit Theorem and the Law of Large Numbers. Conditional probability models. Introduction to random signals. Applications in Electrical and Computer Engineering provided throughout the semester. (Formerly titled: "Probability and Stochastic Processes." Same as CPE 3533 . Credit cannot be earned for both CPE 3533 and EE 3533 .) Generally offered: Fall, Spring. This course has Differential Tuition. Course Fee: DL01 $75.