Chapter 3: Asymptotic Equipartition Property
|
|
- Gerard Gardner
- 5 years ago
- Views:
Transcription
1 Chapter 3: Asymptotic Equipartition Property Chapter 3 outline Strong vs. Weak Typicality Convergence Asymptotic Equipartition Property Theorem High-probability sets and the typical set Consequences of the AEP: data compression
2 Strong versus Weak Typicality Intuition behind typicality? Another example of Typicality Bit-sequences of length n = 8, prob(1) = p (prob(0) = (1-p)) Strong typicality? Weak typicality? What if p=0.5?
3 Convergence of random variables Weak Law of Large Numbers + the AEP
4 Typical sets intuition What s the point? Consider iid bit-strings strings of length N=100, prob(1) = p1=0.1 Probability of a given string X with r ones is Number of strings with r ones is Distribution of r, the # of ones in a string of length N is thus Typical sets intuition n(r)p (x) = ( ) ( ) N r p r 1 (1 p 1 ) N r N = 100 N = r r Consider iid bit-strings strings of length N, prob(1) = p1=0.1
5 Typical sets intuition What s the point? Consider iid bit-strings strings of length N=100, prob(1) = p1=0.1 x log 2 (P (x)) Figure The top 15 strings are samples from X 100, where p 1 = 0.1 and p 0 = 0.9. The bottom two are the most and least probable strings in this ensemble. The final column shows the log-probabilities of the random strings, which may be compared with the entropy H(X 100 ) = 46.9 bits. [Mackay textbook, pg. 78] Definition: weak typicality
6 n(h (X)+) A(n). 2 Finally, for sufficiently large n, Pr{A(n) } (3.12) > 1, so that 1 < Pr{A(n) (3.13) } n(h (X) ) 2 (3.14) permitted. Printing not permitted. Copyright Cambridge University Press On-screen viewing (n) this book for 30 pounds or $50. See for links. You can x Abuy The typical set visually A(n) = 2 n(h (X) ) 4.5: Proofs, (3.15) 81 where the second inequality follows from (3.6). Hence, log2 P (x) n(h (X) ) of length 100, prob(1) = 0.1 Bit sequences A(n), (3.16) (1 )2 which completes the proof of the properties of A(n). 3.2 N H(X) TN β CONSEQUENCES OF THE AEP: DATA COMPRESSION Let X1, X2,..., Xn be independent, identically distributed random variables drawn from the probability mass"function p(x). We wish to find short descriptions for such sequences of random variables. We divide all sequences in X n into two sets: the typical set A(n) and its complement, as shown in Figure 3.1. " " " " n: n elements The asymptotic equipartition principle is equivalent to: Shannon s source Non-typical coding theorem (verbal statement). N i.i.d. ranset dom variables each with entropy H(X) can be compressed into more than N H(X) bits with negligible risk of information loss, as N ; conversely if they are compressed into fewer than N H(X) bits it is virset tually certain that Typical information will be lost. A(n) : 2n(H + ) Most + least likely sequences NOT in typical set Proofsthe second inequality follows from (3.6). Hence where [Mackay pg. 81] elements These two theorems are equivalent because we can define a compression algorithm that gives a distinct name of length N H(X) bits to each x in the typical FIGURE 3.1. Typical sets and source coding. set. [Cover+Thomas pg. 60] Figure Schematic diagram showing all strings in the ensemble X N ranked by their probability, and the typical set TN β. ASYMPTOTIC EQUIPARTITION PROPERTY University of Illinois at Chicago ECE 534, FallThis 2009, Natasha Devroye section may be skipped if found tough going. A(n) 2n(H (X)+). (3.12) The law of large numbers Finally, for sufficiently large n, Pr{A(n) } > 1, so that Our proof of the source coding theorem uses the law of large numbers. (n) (3.13) 1 Pr{A } Mean and variance ofa<real random = u = u P (u)u variable are E[u] 2 2. and var(u) = σu2 = E[(u u ) ] = P (u)(u u ) n(h (X) ) u 2 (3.14) (n) Technical note: strictly x A I am assuming here that u is a function u(x) of finite discrete ensemble X. Then the summations a sample x from a n(h (X) ) (n) Ax P, =2 (x)f (u(x)). This means that(3.15) P (u) u P (u)f (u) should be written is a finite sum of delta functions. This restriction guarantees that the and variance of u follows do exist,from which(3.6). is nothence, necessarily the case for wheremean the second inequality general P (u). Properties of the typical set (n) n(h (X) ) ALet (1 (3.16) Chebyshev s inequality 1. t be a )2 non-negative, real random variable, and let α be a positive real number. Then which completes the proof of the properties of A(n). t P (t α). (4.30) α 3.2 CONSEQUENCES OF THE AEP: DATA COMPRESSION Proof: P (t α) = t α P (t). We multiply each term by t/α 1 and,...,α) Xn be independent, identically random varilet X1, X add thedistributed (non-negative) missing obtain: P2(t t α P (t)t/α. We function ables drawn from the terms and obtain: P (t probability α) t mass P (t)t/α = t /α.p(x). We wish to find short descriptions for such sequences of random variables. We divide all sequences in X n into two sets: the typical set A(n) and its complement, as shown in Figure 3.1. n: n elements Non-typical set Typical set FIGURE 3.1. Typical sets and source coding. [Cover+Thomas pg. 60] A(n) : 2n(H + ) elements
7 n(h (X) ) A(n), (1 )2 (3.16) which completes the proof of the properties of A(n). 3.2 CONSEQUENCES OF THE AEP: DATA COMPRESSION Let X1, X2,..., Xn be independent, identically distributed random variables drawn from the probability mass function p(x). We wish to find short descriptions for such sequences of random variables. We divide all sequences in X n into two sets: the typical set A(n) and its complement, as shown in Figure 3.1. Consequences of the AEP n: n elements Typical set contains almost all the probability Non-typical set Typical set A(n) : 2n(H + ) elements 3.2 CONSEQUENCES OF THE AEP: DATA COMPRESSION 61 FIGURE 3.1. Typical sets and source coding. Non-typical set Description: n log + 2 bits How many are in this set useful for source coding (compression) Typical set Description: n(h + ) + 2 bits FIGURE 3.2. Source code using the typical set. We order all elements in each set according to some order (e.g., lexicographic order). Then we can represent each sequence of A(n) by giving the index of the sequence in the set. Since there are 2n(H +) sequences (n) in A, the indexing requires no more than n(h + ) + 1 bits. [The extra bit may be necessary because n(h + ) may not be an integer.] We prefix all these sequences by a 0, giving a total length of n(h + ) + 2 bits to represent each sequence in A(n) (see Figure 3.2). Similarly, we can index each sequence not in A(n) by using not more than n log X + 1 bits. Prefixing these indices by 1, we have a code for all the sequences in X n. Note the following features of the above coding scheme: Consequences of the AEP The code is one-to-one and easily decodable. The initial bit acts as a flag bit to indicate the length of the codeword that follows. c We have used a brute-force enumeration of the atypical set A(n) without taking into account the fact that the number of elements in c is less than the number of elements in X n. Surprisingly, this is A(n) good enough to yield an efficient description. The typical sequences have short descriptions of length nh. We use the notation x to denote a sequence x, x,..., x. Let l(x ) By enumeration be the length of the codeword corresponding to x. If n is sufficiently n 1 n 2 n n large so that Pr{A(n) } 1, the expected length of the codeword is E(l(X n )) = p(x n )l(x n ) (3.17) xn
8 AEP and data compression ``High-probability set vs. ``typical set Typical set: small number of outcomes that contain most of the probability Is it the smallest such set?
9 Some notation Bit sequences of length 100, prob(1) = 0.1 NH(X) log 2 P (x) T Nβ What s the difference? Why use the ``typical set rather than the ``high-probability set?
Source Coding. Master Universitario en Ingeniería de Telecomunicación. I. Santamaría Universidad de Cantabria
Source Coding Master Universitario en Ingeniería de Telecomunicación I. Santamaría Universidad de Cantabria Contents Introduction Asymptotic Equipartition Property Optimal Codes (Huffman Coding) Universal
More informationLecture 5: Asymptotic Equipartition Property
Lecture 5: Asymptotic Equipartition Property Law of large number for product of random variables AEP and consequences Dr. Yao Xie, ECE587, Information Theory, Duke University Stock market Initial investment
More information(Classical) Information Theory II: Source coding
(Classical) Information Theory II: Source coding Sibasish Ghosh The Institute of Mathematical Sciences CIT Campus, Taramani, Chennai 600 113, India. p. 1 Abstract The information content of a random variable
More informationCommunications Theory and Engineering
Communications Theory and Engineering Master's Degree in Electronic Engineering Sapienza University of Rome A.A. 2018-2019 AEP Asymptotic Equipartition Property AEP In information theory, the analog of
More informationChapter 2 Date Compression: Source Coding. 2.1 An Introduction to Source Coding 2.2 Optimal Source Codes 2.3 Huffman Code
Chapter 2 Date Compression: Source Coding 2.1 An Introduction to Source Coding 2.2 Optimal Source Codes 2.3 Huffman Code 2.1 An Introduction to Source Coding Source coding can be seen as an efficient way
More informationChapter 3 Source Coding. 3.1 An Introduction to Source Coding 3.2 Optimal Source Codes 3.3 Shannon-Fano Code 3.4 Huffman Code
Chapter 3 Source Coding 3. An Introduction to Source Coding 3.2 Optimal Source Codes 3.3 Shannon-Fano Code 3.4 Huffman Code 3. An Introduction to Source Coding Entropy (in bits per symbol) implies in average
More informationData Compression. Part I
Copyright Cambridge University Press 3. On-screen viewing permitted. Printing not permitted. http://www.cambridge.org/56498 Part I Data Compression Copyright Cambridge University Press 3. On-screen viewing
More information4F5: Advanced Communications and Coding Handout 2: The Typical Set, Compression, Mutual Information
4F5: Advanced Communications and Coding Handout 2: The Typical Set, Compression, Mutual Information Ramji Venkataramanan Signal Processing and Communications Lab Department of Engineering ramji.v@eng.cam.ac.uk
More informationEntropy as a measure of surprise
Entropy as a measure of surprise Lecture 5: Sam Roweis September 26, 25 What does information do? It removes uncertainty. Information Conveyed = Uncertainty Removed = Surprise Yielded. How should we quantify
More informationEE376A: Homework #3 Due by 11:59pm Saturday, February 10th, 2018
Please submit the solutions on Gradescope. EE376A: Homework #3 Due by 11:59pm Saturday, February 10th, 2018 1. Optimal codeword lengths. Although the codeword lengths of an optimal variable length code
More informationEECS 229A Spring 2007 * * (a) By stationarity and the chain rule for entropy, we have
EECS 229A Spring 2007 * * Solutions to Homework 3 1. Problem 4.11 on pg. 93 of the text. Stationary processes (a) By stationarity and the chain rule for entropy, we have H(X 0 ) + H(X n X 0 ) = H(X 0,
More informationIntroduction to information theory and coding
Introduction to information theory and coding Louis WEHENKEL Set of slides No 4 Source modeling and source coding Stochastic processes and models for information sources First Shannon theorem : data compression
More informationChapter 9 Fundamental Limits in Information Theory
Chapter 9 Fundamental Limits in Information Theory Information Theory is the fundamental theory behind information manipulation, including data compression and data transmission. 9.1 Introduction o For
More information10-704: Information Processing and Learning Fall Lecture 10: Oct 3
0-704: Information Processing and Learning Fall 206 Lecturer: Aarti Singh Lecture 0: Oct 3 Note: These notes are based on scribed notes from Spring5 offering of this course. LaTeX template courtesy of
More information10-704: Information Processing and Learning Fall Lecture 9: Sept 28
10-704: Information Processing and Learning Fall 2016 Lecturer: Siheng Chen Lecture 9: Sept 28 Note: These notes are based on scribed notes from Spring15 offering of this course. LaTeX template courtesy
More informationSIGNAL COMPRESSION Lecture Shannon-Fano-Elias Codes and Arithmetic Coding
SIGNAL COMPRESSION Lecture 3 4.9.2007 Shannon-Fano-Elias Codes and Arithmetic Coding 1 Shannon-Fano-Elias Coding We discuss how to encode the symbols {a 1, a 2,..., a m }, knowing their probabilities,
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science Transmission of Information Spring 2006
MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science 6.44 Transmission of Information Spring 2006 Homework 2 Solution name username April 4, 2006 Reading: Chapter
More informationInformation Theory. David Rosenberg. June 15, New York University. David Rosenberg (New York University) DS-GA 1003 June 15, / 18
Information Theory David Rosenberg New York University June 15, 2015 David Rosenberg (New York University) DS-GA 1003 June 15, 2015 1 / 18 A Measure of Information? Consider a discrete random variable
More informationChapter 5: Data Compression
Chapter 5: Data Compression Definition. A source code C for a random variable X is a mapping from the range of X to the set of finite length strings of symbols from a D-ary alphabet. ˆX: source alphabet,
More informationEE5139R: Problem Set 4 Assigned: 31/08/16, Due: 07/09/16
EE539R: Problem Set 4 Assigned: 3/08/6, Due: 07/09/6. Cover and Thomas: Problem 3.5 Sets defined by probabilities: Define the set C n (t = {x n : P X n(x n 2 nt } (a We have = P X n(x n P X n(x n 2 nt
More informationLecture 22: Final Review
Lecture 22: Final Review Nuts and bolts Fundamental questions and limits Tools Practical algorithms Future topics Dr Yao Xie, ECE587, Information Theory, Duke University Basics Dr Yao Xie, ECE587, Information
More informationMARKOV CHAINS A finite state Markov chain is a sequence of discrete cv s from a finite alphabet where is a pmf on and for
MARKOV CHAINS A finite state Markov chain is a sequence S 0,S 1,... of discrete cv s from a finite alphabet S where q 0 (s) is a pmf on S 0 and for n 1, Q(s s ) = Pr(S n =s S n 1 =s ) = Pr(S n =s S n 1
More informationEE376A: Homework #2 Solutions Due by 11:59pm Thursday, February 1st, 2018
Please submit the solutions on Gradescope. Some definitions that may be useful: EE376A: Homework #2 Solutions Due by 11:59pm Thursday, February 1st, 2018 Definition 1: A sequence of random variables X
More information(Classical) Information Theory III: Noisy channel coding
(Classical) Information Theory III: Noisy channel coding Sibasish Ghosh The Institute of Mathematical Sciences CIT Campus, Taramani, Chennai 600 113, India. p. 1 Abstract What is the best possible way
More informationEE376A - Information Theory Midterm, Tuesday February 10th. Please start answering each question on a new page of the answer booklet.
EE376A - Information Theory Midterm, Tuesday February 10th Instructions: You have two hours, 7PM - 9PM The exam has 3 questions, totaling 100 points. Please start answering each question on a new page
More informationExample: Letter Frequencies
Example: Letter Frequencies i a i p i 1 a 0.0575 2 b 0.0128 3 c 0.0263 4 d 0.0285 5 e 0.0913 6 f 0.0173 7 g 0.0133 8 h 0.0313 9 i 0.0599 10 j 0.0006 11 k 0.0084 12 l 0.0335 13 m 0.0235 14 n 0.0596 15 o
More informationExample: Letter Frequencies
Example: Letter Frequencies i a i p i 1 a 0.0575 2 b 0.0128 3 c 0.0263 4 d 0.0285 5 e 0.0913 6 f 0.0173 7 g 0.0133 8 h 0.0313 9 i 0.0599 10 j 0.0006 11 k 0.0084 12 l 0.0335 13 m 0.0235 14 n 0.0596 15 o
More informationAn instantaneous code (prefix code, tree code) with the codeword lengths l 1,..., l N exists if and only if. 2 l i. i=1
Kraft s inequality An instantaneous code (prefix code, tree code) with the codeword lengths l 1,..., l N exists if and only if N 2 l i 1 Proof: Suppose that we have a tree code. Let l max = max{l 1,...,
More informationU Logo Use Guidelines
COMP2610/6261 - Information Theory Lecture 15: Arithmetic Coding U Logo Use Guidelines Mark Reid and Aditya Menon logo is a contemporary n of our heritage. presents our name, d and our motto: arn the nature
More informationNetwork coding for multicast relation to compression and generalization of Slepian-Wolf
Network coding for multicast relation to compression and generalization of Slepian-Wolf 1 Overview Review of Slepian-Wolf Distributed network compression Error exponents Source-channel separation issues
More informationECE 587 / STA 563: Lecture 5 Lossless Compression
ECE 587 / STA 563: Lecture 5 Lossless Compression Information Theory Duke University, Fall 2017 Author: Galen Reeves Last Modified: October 18, 2017 Outline of lecture: 5.1 Introduction to Lossless Source
More informationECE 587 / STA 563: Lecture 5 Lossless Compression
ECE 587 / STA 563: Lecture 5 Lossless Compression Information Theory Duke University, Fall 28 Author: Galen Reeves Last Modified: September 27, 28 Outline of lecture: 5. Introduction to Lossless Source
More informationData Compression. Limit of Information Compression. October, Examples of codes 1
Data Compression Limit of Information Compression Radu Trîmbiţaş October, 202 Outline Contents Eamples of codes 2 Kraft Inequality 4 2. Kraft Inequality............................ 4 2.2 Kraft inequality
More informationInformation Theory. Week 4 Compressing streams. Iain Murray,
Information Theory http://www.inf.ed.ac.uk/teaching/courses/it/ Week 4 Compressing streams Iain Murray, 2014 School of Informatics, University of Edinburgh Jensen s inequality For convex functions: E[f(x)]
More informationEntropies & Information Theory
Entropies & Information Theory LECTURE I Nilanjana Datta University of Cambridge,U.K. See lecture notes on: http://www.qi.damtp.cam.ac.uk/node/223 Quantum Information Theory Born out of Classical Information
More informationEE376A - Information Theory Final, Monday March 14th 2016 Solutions. Please start answering each question on a new page of the answer booklet.
EE376A - Information Theory Final, Monday March 14th 216 Solutions Instructions: You have three hours, 3.3PM - 6.3PM The exam has 4 questions, totaling 12 points. Please start answering each question on
More informationData Compression Techniques (Spring 2012) Model Solutions for Exercise 2
582487 Data Compression Techniques (Spring 22) Model Solutions for Exercise 2 If you have any feedback or corrections, please contact nvalimak at cs.helsinki.fi.. Problem: Construct a canonical prefix
More informationExample: Letter Frequencies
Example: Letter Frequencies i a i p i 1 a 0.0575 2 b 0.0128 3 c 0.0263 4 d 0.0285 5 e 0.0913 6 f 0.0173 7 g 0.0133 8 h 0.0313 9 i 0.0599 10 j 0.0006 11 k 0.0084 12 l 0.0335 13 m 0.0235 14 n 0.0596 15 o
More information1 Introduction to information theory
1 Introduction to information theory 1.1 Introduction In this chapter we present some of the basic concepts of information theory. The situations we have in mind involve the exchange of information through
More informationLecture 11: Quantum Information III - Source Coding
CSCI5370 Quantum Computing November 25, 203 Lecture : Quantum Information III - Source Coding Lecturer: Shengyu Zhang Scribe: Hing Yin Tsang. Holevo s bound Suppose Alice has an information source X that
More informationExercises with solutions (Set B)
Exercises with solutions (Set B) 3. A fair coin is tossed an infinite number of times. Let Y n be a random variable, with n Z, that describes the outcome of the n-th coin toss. If the outcome of the n-th
More informationLecture 3: Channel Capacity
Lecture 3: Channel Capacity 1 Definitions Channel capacity is a measure of maximum information per channel usage one can get through a channel. This one of the fundamental concepts in information theory.
More informationBandwidth: Communicate large complex & highly detailed 3D models through lowbandwidth connection (e.g. VRML over the Internet)
Compression Motivation Bandwidth: Communicate large complex & highly detailed 3D models through lowbandwidth connection (e.g. VRML over the Internet) Storage: Store large & complex 3D models (e.g. 3D scanner
More informationHomework Set #2 Data Compression, Huffman code and AEP
Homework Set #2 Data Compression, Huffman code and AEP 1. Huffman coding. Consider the random variable ( x1 x X = 2 x 3 x 4 x 5 x 6 x 7 0.50 0.26 0.11 0.04 0.04 0.03 0.02 (a Find a binary Huffman code
More informationECE 4400:693 - Information Theory
ECE 4400:693 - Information Theory Dr. Nghi Tran Lecture 8: Differential Entropy Dr. Nghi Tran (ECE-University of Akron) ECE 4400:693 Lecture 1 / 43 Outline 1 Review: Entropy of discrete RVs 2 Differential
More informationMotivation for Arithmetic Coding
Motivation for Arithmetic Coding Motivations for arithmetic coding: 1) Huffman coding algorithm can generate prefix codes with a minimum average codeword length. But this length is usually strictly greater
More informationInformation Theory: Entropy, Markov Chains, and Huffman Coding
The University of Notre Dame A senior thesis submitted to the Department of Mathematics and the Glynn Family Honors Program Information Theory: Entropy, Markov Chains, and Huffman Coding Patrick LeBlanc
More informationEE5319R: Problem Set 3 Assigned: 24/08/16, Due: 31/08/16
EE539R: Problem Set 3 Assigned: 24/08/6, Due: 3/08/6. Cover and Thomas: Problem 2.30 (Maimum Entropy): Solution: We are required to maimize H(P X ) over all distributions P X on the non-negative integers
More informationMultimedia Communications. Mathematical Preliminaries for Lossless Compression
Multimedia Communications Mathematical Preliminaries for Lossless Compression What we will see in this chapter Definition of information and entropy Modeling a data source Definition of coding and when
More informationLec 05 Arithmetic Coding
ECE 5578 Multimedia Communication Lec 05 Arithmetic Coding Zhu Li Dept of CSEE, UMKC web: http://l.web.umkc.edu/lizhu phone: x2346 Z. Li, Multimedia Communciation, 208 p. Outline Lecture 04 ReCap Arithmetic
More informationEE5585 Data Compression January 29, Lecture 3. x X x X. 2 l(x) 1 (1)
EE5585 Data Compression January 29, 2013 Lecture 3 Instructor: Arya Mazumdar Scribe: Katie Moenkhaus Uniquely Decodable Codes Recall that for a uniquely decodable code with source set X, if l(x) is the
More informationLecture 16. Error-free variable length schemes (contd.): Shannon-Fano-Elias code, Huffman code
Lecture 16 Agenda for the lecture Error-free variable length schemes (contd.): Shannon-Fano-Elias code, Huffman code Variable-length source codes with error 16.1 Error-free coding schemes 16.1.1 The Shannon-Fano-Elias
More informationCapacity of a channel Shannon s second theorem. Information Theory 1/33
Capacity of a channel Shannon s second theorem Information Theory 1/33 Outline 1. Memoryless channels, examples ; 2. Capacity ; 3. Symmetric channels ; 4. Channel Coding ; 5. Shannon s second theorem,
More informationCoding of memoryless sources 1/35
Coding of memoryless sources 1/35 Outline 1. Morse coding ; 2. Definitions : encoding, encoding efficiency ; 3. fixed length codes, encoding integers ; 4. prefix condition ; 5. Kraft and Mac Millan theorems
More informationCOS597D: Information Theory in Computer Science October 19, Lecture 10
COS597D: Information Theory in Computer Science October 9, 20 Lecture 0 Lecturer: Mark Braverman Scribe: Andrej Risteski Kolmogorov Complexity In the previous lectures, we became acquainted with the concept
More informationShannon s Noisy-Channel Coding Theorem
Shannon s Noisy-Channel Coding Theorem Lucas Slot Sebastian Zur February 13, 2015 Lucas Slot, Sebastian Zur Shannon s Noisy-Channel Coding Theorem February 13, 2015 1 / 29 Outline 1 Definitions and Terminology
More informationLECTURE 15. Last time: Feedback channel: setting up the problem. Lecture outline. Joint source and channel coding theorem
LECTURE 15 Last time: Feedback channel: setting up the problem Perfect feedback Feedback capacity Data compression Lecture outline Joint source and channel coding theorem Converse Robustness Brain teaser
More informationELEC 515 Information Theory. Distortionless Source Coding
ELEC 515 Information Theory Distortionless Source Coding 1 Source Coding Output Alphabet Y={y 1,,y J } Source Encoder Lengths 2 Source Coding Two coding requirements The source sequence can be recovered
More informationEE5585 Data Compression May 2, Lecture 27
EE5585 Data Compression May 2, 2013 Lecture 27 Instructor: Arya Mazumdar Scribe: Fangying Zhang Distributed Data Compression/Source Coding In the previous class we used a H-W table as a simple example,
More informationLECTURE 13. Last time: Lecture outline
LECTURE 13 Last time: Strong coding theorem Revisiting channel and codes Bound on probability of error Error exponent Lecture outline Fano s Lemma revisited Fano s inequality for codewords Converse to
More informationPROBABILITY AND INFORMATION THEORY. Dr. Gjergji Kasneci Introduction to Information Retrieval WS
PROBABILITY AND INFORMATION THEORY Dr. Gjergji Kasneci Introduction to Information Retrieval WS 2012-13 1 Outline Intro Basics of probability and information theory Probability space Rules of probability
More informationPART III. Outline. Codes and Cryptography. Sources. Optimal Codes (I) Jorge L. Villar. MAMME, Fall 2015
Outline Codes and Cryptography 1 Information Sources and Optimal Codes 2 Building Optimal Codes: Huffman Codes MAMME, Fall 2015 3 Shannon Entropy and Mutual Information PART III Sources Information source:
More informationEntropy and Ergodic Theory Lecture 3: The meaning of entropy in information theory
Entropy and Ergodic Theory Lecture 3: The meaning of entropy in information theory 1 The intuitive meaning of entropy Modern information theory was born in Shannon s 1948 paper A Mathematical Theory of
More informationInformation Theory. M1 Informatique (parcours recherche et innovation) Aline Roumy. January INRIA Rennes 1/ 73
1/ 73 Information Theory M1 Informatique (parcours recherche et innovation) Aline Roumy INRIA Rennes January 2018 Outline 2/ 73 1 Non mathematical introduction 2 Mathematical introduction: definitions
More informationChapter 5. Data Compression
Chapter 5 Data Compression Peng-Hua Wang Graduate Inst. of Comm. Engineering National Taipei University Chapter Outline Chap. 5 Data Compression 5.1 Example of Codes 5.2 Kraft Inequality 5.3 Optimal Codes
More informationLecture 1: September 25, A quick reminder about random variables and convexity
Information and Coding Theory Autumn 207 Lecturer: Madhur Tulsiani Lecture : September 25, 207 Administrivia This course will cover some basic concepts in information and coding theory, and their applications
More informationInformation Theory. Coding and Information Theory. Information Theory Textbooks. Entropy
Coding and Information Theory Chris Williams, School of Informatics, University of Edinburgh Overview What is information theory? Entropy Coding Information Theory Shannon (1948): Information theory is
More informationSome Basic Concepts of Probability and Information Theory: Pt. 2
Some Basic Concepts of Probability and Information Theory: Pt. 2 PHYS 476Q - Southern Illinois University January 22, 2018 PHYS 476Q - Southern Illinois University Some Basic Concepts of Probability and
More informationOptimal codes - I. A code is optimal if it has the shortest codeword length L. i i. This can be seen as an optimization problem. min.
Huffman coding Optimal codes - I A code is optimal if it has the shortest codeword length L L m = i= pl i i This can be seen as an optimization problem min i= li subject to D m m i= lp Gabriele Monfardini
More informationLecture 3. Mathematical methods in communication I. REMINDER. A. Convex Set. A set R is a convex set iff, x 1,x 2 R, θ, 0 θ 1, θx 1 + θx 2 R, (1)
3- Mathematical methods in communication Lecture 3 Lecturer: Haim Permuter Scribe: Yuval Carmel, Dima Khaykin, Ziv Goldfeld I. REMINDER A. Convex Set A set R is a convex set iff, x,x 2 R, θ, θ, θx + θx
More information4. Quantization and Data Compression. ECE 302 Spring 2012 Purdue University, School of ECE Prof. Ilya Pollak
4. Quantization and Data Compression ECE 32 Spring 22 Purdue University, School of ECE Prof. What is data compression? Reducing the file size without compromising the quality of the data stored in the
More informationChapter 4. Data Transmission and Channel Capacity. Po-Ning Chen, Professor. Department of Communications Engineering. National Chiao Tung University
Chapter 4 Data Transmission and Channel Capacity Po-Ning Chen, Professor Department of Communications Engineering National Chiao Tung University Hsin Chu, Taiwan 30050, R.O.C. Principle of Data Transmission
More informationInformation Theory. Lecture 5 Entropy rate and Markov sources STEFAN HÖST
Information Theory Lecture 5 Entropy rate and Markov sources STEFAN HÖST Universal Source Coding Huffman coding is optimal, what is the problem? In the previous coding schemes (Huffman and Shannon-Fano)it
More informationlossless, optimal compressor
6. Variable-length Lossless Compression The principal engineering goal of compression is to represent a given sequence a, a 2,..., a n produced by a source as a sequence of bits of minimal possible length.
More informationLecture 11: Polar codes construction
15-859: Information Theory and Applications in TCS CMU: Spring 2013 Lecturer: Venkatesan Guruswami Lecture 11: Polar codes construction February 26, 2013 Scribe: Dan Stahlke 1 Polar codes: recap of last
More informationCSCI 2570 Introduction to Nanocomputing
CSCI 2570 Introduction to Nanocomputing Information Theory John E Savage What is Information Theory Introduced by Claude Shannon. See Wikipedia Two foci: a) data compression and b) reliable communication
More informationLecture 1: Shannon s Theorem
Lecture 1: Shannon s Theorem Lecturer: Travis Gagie January 13th, 2015 Welcome to Data Compression! I m Travis and I ll be your instructor this week. If you haven t registered yet, don t worry, we ll work
More informationChapter 2: Source coding
Chapter 2: meghdadi@ensil.unilim.fr University of Limoges Chapter 2: Entropy of Markov Source Chapter 2: Entropy of Markov Source Markov model for information sources Given the present, the future is independent
More informationELEC546 Review of Information Theory
ELEC546 Review of Information Theory Vincent Lau 1/1/004 1 Review of Information Theory Entropy: Measure of uncertainty of a random variable X. The entropy of X, H(X), is given by: If X is a discrete random
More informationLecture 3 : Algorithms for source coding. September 30, 2016
Lecture 3 : Algorithms for source coding September 30, 2016 Outline 1. Huffman code ; proof of optimality ; 2. Coding with intervals : Shannon-Fano-Elias code and Shannon code ; 3. Arithmetic coding. 1/39
More informationLecture 17: Differential Entropy
Lecture 17: Differential Entropy Differential entropy AEP for differential entropy Quantization Maximum differential entropy Estimation counterpart of Fano s inequality Dr. Yao Xie, ECE587, Information
More informationCS 229r Information Theory in Computer Science Feb 12, Lecture 5
CS 229r Information Theory in Computer Science Feb 12, 2019 Lecture 5 Instructor: Madhu Sudan Scribe: Pranay Tankala 1 Overview A universal compression algorithm is a single compression algorithm applicable
More informationShannon s Noisy-Channel Coding Theorem
Shannon s Noisy-Channel Coding Theorem Lucas Slot Sebastian Zur February 2015 Abstract In information theory, Shannon s Noisy-Channel Coding Theorem states that it is possible to communicate over a noisy
More informationLecture 8: Channel and source-channel coding theorems; BEC & linear codes. 1 Intuitive justification for upper bound on channel capacity
5-859: Information Theory and Applications in TCS CMU: Spring 23 Lecture 8: Channel and source-channel coding theorems; BEC & linear codes February 7, 23 Lecturer: Venkatesan Guruswami Scribe: Dan Stahlke
More informationSource Coding and Function Computation: Optimal Rate in Zero-Error and Vanishing Zero-Error Regime
Source Coding and Function Computation: Optimal Rate in Zero-Error and Vanishing Zero-Error Regime Solmaz Torabi Dept. of Electrical and Computer Engineering Drexel University st669@drexel.edu Advisor:
More informationLecture 2: August 31
0-704: Information Processing and Learning Fall 206 Lecturer: Aarti Singh Lecture 2: August 3 Note: These notes are based on scribed notes from Spring5 offering of this course. LaTeX template courtesy
More informationCOMM901 Source Coding and Compression. Quiz 1
German University in Cairo - GUC Faculty of Information Engineering & Technology - IET Department of Communication Engineering Winter Semester 2013/2014 Students Name: Students ID: COMM901 Source Coding
More informationFundamental Tools - Probability Theory IV
Fundamental Tools - Probability Theory IV MSc Financial Mathematics The University of Warwick October 1, 2015 MSc Financial Mathematics Fundamental Tools - Probability Theory IV 1 / 14 Model-independent
More informationThe binary entropy function
ECE 7680 Lecture 2 Definitions and Basic Facts Objective: To learn a bunch of definitions about entropy and information measures that will be useful through the quarter, and to present some simple but
More informationLecture 4: September Reminder: convergence of sequences
36-705: Intermediate Statistics Fall 2017 Lecturer: Siva Balakrishnan Lecture 4: September 6 In this lecture we discuss the convergence of random variables. At a high-level, our first few lectures focused
More informationA One-to-One Code and Its Anti-Redundancy
A One-to-One Code and Its Anti-Redundancy W. Szpankowski Department of Computer Science, Purdue University July 4, 2005 This research is supported by NSF, NSA and NIH. Outline of the Talk. Prefix Codes
More informationCapacity of AWGN channels
Chapter 3 Capacity of AWGN channels In this chapter we prove that the capacity of an AWGN channel with bandwidth W and signal-tonoise ratio SNR is W log 2 (1+SNR) bits per second (b/s). The proof that
More informationCOMP2610/COMP Information Theory
COMP2610/COMP6261 - Information Theory Lecture 9: Probabilistic Inequalities Mark Reid and Aditya Menon Research School of Computer Science The Australian National University August 19th, 2014 Mark Reid
More informationOn the Cost of Worst-Case Coding Length Constraints
On the Cost of Worst-Case Coding Length Constraints Dror Baron and Andrew C. Singer Abstract We investigate the redundancy that arises from adding a worst-case length-constraint to uniquely decodable fixed
More informationComplex Systems Methods 2. Conditional mutual information, entropy rate and algorithmic complexity
Complex Systems Methods 2. Conditional mutual information, entropy rate and algorithmic complexity Eckehard Olbrich MPI MiS Leipzig Potsdam WS 2007/08 Olbrich (Leipzig) 26.10.2007 1 / 18 Overview 1 Summary
More informationChapter 2: Entropy and Mutual Information. University of Illinois at Chicago ECE 534, Natasha Devroye
Chapter 2: Entropy and Mutual Information Chapter 2 outline Definitions Entropy Joint entropy, conditional entropy Relative entropy, mutual information Chain rules Jensen s inequality Log-sum inequality
More informationSolutions to Set #2 Data Compression, Huffman code and AEP
Solutions to Set #2 Data Compression, Huffman code and AEP. Huffman coding. Consider the random variable ( ) x x X = 2 x 3 x 4 x 5 x 6 x 7 0.50 0.26 0. 0.04 0.04 0.03 0.02 (a) Find a binary Huffman code
More informationLecture 1 : Data Compression and Entropy
CPS290: Algorithmic Foundations of Data Science January 8, 207 Lecture : Data Compression and Entropy Lecturer: Kamesh Munagala Scribe: Kamesh Munagala In this lecture, we will study a simple model for
More informationBasic Principles of Lossless Coding. Universal Lossless coding. Lempel-Ziv Coding. 2. Exploit dependences between successive symbols.
Universal Lossless coding Lempel-Ziv Coding Basic principles of lossless compression Historical review Variable-length-to-block coding Lempel-Ziv coding 1 Basic Principles of Lossless Coding 1. Exploit
More informationLecture 5 Channel Coding over Continuous Channels
Lecture 5 Channel Coding over Continuous Channels I-Hsiang Wang Department of Electrical Engineering National Taiwan University ihwang@ntu.edu.tw November 14, 2014 1 / 34 I-Hsiang Wang NIT Lecture 5 From
More information