Probability & Stochastic Processes

Catalog description

This course starts with a review of elements of probability theory such as: axioms of probability, conditional and independent probabilities, random variables, distribution functions, functions of random variables, expectations, and some well-known random variables such as Bernoulli, geometry, binomial, Pascal, Gaussian, and Poisson. The course introduces more advanced topics such as stochastic processes, stationary processes, correlations, statistical signal processing, and well-known processes such as Brownian motion, Gaussian, Poisson, and Markov. Classroom Hours- Laboratory and/or Studio Hours- Course Credits: 3-0-3