Coding Theory Autumn 2017 Lecture 6 : October 16 , 2017
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منابع مشابه
STA 563 : Lecture 9 – Rate Distortion Information Theory
9.1 Motivation: Quantization of Random Variables . . . . . . . . . . . . . . . . . . . . . 1 9.2 Lossy Source Coding Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 9.3 The Rate Distortion Coding Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . 4 9.3.1 Example: Binary Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 9.3.2 Example: Gaussian ...
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Disclaimer 3 1. Why we need measure theory 4 1.1. Riddle 1 4 1.2. Riddle 2 4 1.3. Riddle 3 4 1.4. Why we need measure theory 4 2. Measure theory 6 2.1. π-systems, algebras and sigma-algebras 6 3. Carathéodory’s Theorem and constructing measures 8 4. Events and random variables 10 5. Independence 13 6. The tail sigma-algebra 16 7. Expectations 19 8. A strong law of large numbers and the Chernoff...
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