Courses:

Stochastic Processes, Detection, and Estimation >> Content Detail



Recitations



Recitations


SES #TOPICS
R1Course Information; Review of Linear Algebra (PDF)
R2Diagonalization of Symmetric Matrices; Symmetric Positive Definite and Semidefinite Matrices (PDF)
R3More on Symmetric Positive Definite Matrices; Hypothesis Testing for Gaussian Random Vectors (PDF)
R4Binary Hypothesis Tests: Receiver Operating Characteristic (ROC); Geometry of M-ary Hypothesis Tests (PDF)
R5Bayes' Least Squares Estimation; Vector Spaces and Linear Least Squares (PDF)
R6Nonrandom Parameter Estimation (PDF)
R7Linear Systems Review (PDF)
R8Examples of Stochastic Processes; Second Order Statistics and Stochastic Processes (PDF)
R9Discrete Time Processes and Linear Systems; Discrete Time Karhunen–Loeve Expansion (PDF)
R10Binary Detection in White Gaussian Noise; Detection and Estimation in Colored Gaussian Noise (PDF)
R11Linear Detection from Continuous Time Processes; Karhunen–Loeve Expansions and Whitening Filters (PDF)
R12Discrete–Time Wiener Filtering; Prediction and Smoothing (PDF)
R13State Space Models and Kalman Filtering (PDF)
R14Estimation and Detection Using Periodograms (PDF)

 








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