Probability and Stochastic Processes

Catalog description

This course starts with a review of the elements of probability theory such as: axioms of probability, conditional and independent probabilities, random variables, distribution functions, functions of random variables, statistical averages, 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, Poisson, Gaussian, and Markov. Prerequisite: Undergraduate level knowledge of probability theory.