Signals, Information, and Algorithms Laboratory
Professor Gregory W. Wornell



6.432 Stochastic Processes, Detection and Estimation

A. S. Willsky and G. W. Wornell

Fundamentals of detection and estimation for signal processing, communications, and control. Vector spaces of random
variables. Bayesian and Neyman-Pearson hypothesis testing. Bayesian and nonrandom parameter estimation. Minimum-variance unbiased estimators and the Cramer-Rao bounds. Representations for stochastic processes; shaping and whitening filters; Karhunen-Loeve expansions. Detection and estimation from waveform observations. Advanced topics; linear prediction and spectral estimation; Wiener and Kalman filters.