Stochastic Processes, Detection, and Estimation
MIT OpenCourseWare's advanced graduate course covers the mathematical foundations of detection and estimation theory for signal processing, communications, and control systems. Topics include vector spaces of random variables, Bayesian and Neyman-Pearson hypothesis testing, Bayesian and nonrandom parameter estimation, minimum-variance unbiased estimators, and Cramer-Rao bounds. The course also treats representations of stochastic processes, shaping and whitening filters, and Karhunen-Loeve expansions, moving into detection and estimation from waveform observations. Advanced material covers linear prediction, spectral estimation, and Wiener and Kalman filtering. Materials include lecture notes, problem sets, and exams drawn from MIT's Electrical Engineering and Computer Science curriculum, offered free under a Creative Commons license with no certificate attached. The course assumes strong prior background in probability and linear systems.