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COURSE NOTES
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.
Chapter 1 : Probability,
Random Vectors, and Vector Spaces
Chapter 2 : Detection
Theory, Decision Theory, and Hypothesis Testing
Chapter 3 : Estimation
Theory
Chapter 4 : Stochastic
Processes and Systems
Chapter 5 : Karhunen-Loeve
and Sampled Signal Expansions
Chapter 6 : Detection
and Estimation from Waveforms
Chapter 7 : Waveform
Estimation, Wiener and Kalman Filtering
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