Question 2. Please describe your understanding of the following figure WYNI ed

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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Question 2. Please describe your understanding of the following
figure
14 Me ia
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Transcribed Image Text:Question 2. Please describe your understanding of the following figure 14 Me ia Fooe la Digigrlital yhast leey wed
ILScript v31.pdf - SumatraPDF
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Preface
Shannon's Measure of Information
Revien of Probabity Theory
E Data Compression: Eficient Coding of a Single Random
Data Compression EHicient Coding of an Information S
Stochestic Processes and Entropy Rate
Data Compression: Eficient Coding of Sources with Men
Optimizing Probability Vectors ever Concave Functione
Gambling and Hose Betting
Deta Trensmission over a Noisy Digital Channel
Computing Capacity
Convolutional Codes
e Enor Esponent and Channel Reiebilty Function
Joint Source and Channel Coding
Continueus Random Variables and Dferential Entropy
E The Gausian Channel
Bandimited Channds
Parallel Gausian Channels
Arymptotic Equipartition Proparty and Weak Typicality
E Cryptography
Gaussien Random Variables
Grusien Vectors
Stochastic Processes
Bibliegraphy
- List of Figures
- List of Tables
Inde
1.4. Mutual Information
21
I(X;Y)
H(X)
H(Y)
H(X|Y)
H(Y|X)
Union
H(X,Y)
Figure 1.4: Diagram depicting mutual information and entropy in a set-theory
way of thinking.
712 PM
* i ENG
10/17/2013
Transcribed Image Text:ILScript v31.pdf - SumatraPDF File View Go To Zoom Favorites Settings Help Page Bookmarks Preface Shannon's Measure of Information Revien of Probabity Theory E Data Compression: Eficient Coding of a Single Random Data Compression EHicient Coding of an Information S Stochestic Processes and Entropy Rate Data Compression: Eficient Coding of Sources with Men Optimizing Probability Vectors ever Concave Functione Gambling and Hose Betting Deta Trensmission over a Noisy Digital Channel Computing Capacity Convolutional Codes e Enor Esponent and Channel Reiebilty Function Joint Source and Channel Coding Continueus Random Variables and Dferential Entropy E The Gausian Channel Bandimited Channds Parallel Gausian Channels Arymptotic Equipartition Proparty and Weak Typicality E Cryptography Gaussien Random Variables Grusien Vectors Stochastic Processes Bibliegraphy - List of Figures - List of Tables Inde 1.4. Mutual Information 21 I(X;Y) H(X) H(Y) H(X|Y) H(Y|X) Union H(X,Y) Figure 1.4: Diagram depicting mutual information and entropy in a set-theory way of thinking. 712 PM * i ENG 10/17/2013
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