EE 322: Probabilistic Methods for Electrical Engineers ¶

Course Title: Probabilistic Methods for Electrical Engineers
Description: Introduction to probability with applications to electrical engineers. Sets and events, probability space, conditional probability, total probability and Bayes’ rule. Discrete and continuous random variables, cumulative distribution function, probability mass and density functions, expectation, moments, moment generating function, multiple random variables, functions of random variables. Elements of statistics, hypothesis testing, confidence intervals, least squares. Introduction to random processes (Electrical Engineering (E E) | Iowa State University Catalog).

• Set theory basics, Conditional probability, Total probability, Bayes rule, Independence, Counting, Binomial, Reliability
• Discrete random variables (r.v.): Probability Mass Function (PMF), expectation, mean, variance, two random variables(r.v.’s) (joint PMF, conditioning, Bayes, independence), functions of r.v.’s
• Continuous random variables: Probability Density Function (PDF), Cumulative Density Function (CDF), expectation, mean, variance, functions of r.v.’s, two random variables (joint PDF, joint CDF, conditioning, Bayes, independence), derived distributions
• Sums of r.v.’s, Moment Generating Functions
• Covariance, correlation, Bayesian least squares estimation, linear least squares (LS)
• Markov, Chebyshev inequalities, Weak law of large numbers, Central Limit theorem
• Distinguish between discrete and continuous random variables and describe how they relate to engineering problems.
• Define probability mass functions (PMF) and probability density functions (PDF) for discrete and continuous random variables.
• Understand the concepts of joint, marginal, and conditional distributions for multiple random variables.
• Solve problems involving the calculation of joint and marginal probabilities, particularly in multivariable engineering systems.
• The concept of parameter estimation and confidence intervals.