EE 194-01 Advanced Probability, Stochastic Processes, and Estimation

Review of set theory and development of measure-theoretic probability models; Conditional expectation and orthogonally of random variables; Random vectors including second order characterization; Estimation including Bayes least squares, maximum a posteriori, and maximum likelihood methods; Random processes including notions of stationarity, wide sense stationarity, and independent increments; Bernoulli process, Poisson process, Markov processes including Markov chains, Weiner processes; Wide sense stationary processes and linear systems including power spectral density, spectral factorization, noncausal and causal Weiner filters; Mean square stochastic calculus including Karhunen-Loeve decompositions.

Prerequisites: None.

Back to Courses