Stochastic Processes

This course introduces random processes and their applications from a discrete-time point of view, and discusses the continuous-time case when necessary. The course covers the basic concepts of random variables, random vectors, stochastic processes, and random fields. It moves on to common random processes including the white noise, Gaussian processes, Markov processes, Poisson processes, and Markov random fields. Advanced topics are also covered including estimation theory and optimal filtering including linear prediction, Wiener and Kalman filtering, linear models and spectrum estimation.