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ON APPLIED DECISION THEORY,
STANFORD UNIV CA STANFORD ELECTRONICS LABS
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This report is based on material from a Stanford University course EE 252 on the application of statistical decision theory to signal detection, parameter estimation, and pattern recognition. The report is divided into nine parts 1 Filters and Noise 2 Sample Value Representation 3 Detection of a Known Signal in Noise 4 General Dual-Hypothesis Test 5 Signal Detection with Mismatched Filters 6 Detection of Signals with Random Parameters 7 Test of a Finite Number of Hypothesis 8 Estimation 9 Discrete Wiener Filters. Part One provides a general review of classical methods of signal detection and parameter estimation. The rest of the report deals with the application of statistical decision theory to these problems. Author
APPROVED FOR PUBLIC RELEASE