From the MIT on-line course catalog...
6.432 Stochastic Processes, Detection, and Estimation
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.