Stochastic Processes, Detection, and Estimation

Recitations

SES #	TOPICS
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)