6.432 | Spring 2004 | Graduate
Stochastic Processes, Detection, and Estimation


R1 Course Information; Review of Linear Algebra (PDF)
R2 Diagonalization of Symmetric Matrices; Symmetric Positive Definite and Semidefinite Matrices (PDF)
R3 More on Symmetric Positive Definite Matrices; Hypothesis Testing for Gaussian Random Vectors (PDF)
R4 Binary Hypothesis Tests: Receiver Operating Characteristic (ROC); Geometry of M-ary Hypothesis Tests (PDF)
R5 Bayes’ Least Squares Estimation; Vector Spaces and Linear Least Squares (PDF)
R6 Nonrandom Parameter Estimation (PDF)
R7 Linear Systems Review (PDF)
R8 Examples of Stochastic Processes; Second Order Statistics and Stochastic Processes (PDF)
R9 Discrete Time Processes and Linear Systems; Discrete Time Karhunen–Loeve Expansion (PDF)
R10 Binary Detection in White Gaussian Noise; Detection and Estimation in Colored Gaussian Noise (PDF)
R11 Linear Detection from Continuous Time Processes; Karhunen–Loeve Expansions and Whitening Filters (PDF)
R12 Discrete–Time Wiener Filtering; Prediction and Smoothing (PDF)
R13 State Space Models and Kalman Filtering (PDF)
R14 Estimation and Detection Using Periodograms (PDF)