Elements of Information Theory
E641826
Elements of Information Theory is a foundational textbook that systematically develops the theory and applications of information theory, widely used in communications, coding, and data science.
All labels observed (2)
| Label | Occurrences |
|---|---|
| "Elements of Information Theory" | 3 |
| Elements of Information Theory canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T7115723 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elements of Information Theory Context triple: [Thomas M. Cover, notableWork, Elements of Information Theory]
-
A.
Mathematical Foundations of Information Theory
Mathematical Foundations of Information Theory is a seminal monograph by Aleksandr Khinchin that rigorously develops the probabilistic and mathematical basis of Shannon’s information theory.
-
B.
An Introduction to Information Theory: Symbols, Signals and Noise
An Introduction to Information Theory: Symbols, Signals and Noise is a classic, accessible textbook that explains the fundamental concepts of information theory, communication, and coding for a broad scientific and engineering audience.
-
C.
Coding and Information Theory
"Coding and Information Theory" is a foundational textbook by Richard W. Hamming that introduces the mathematical principles underlying error-correcting codes and the transmission of information.
-
D.
information theory
Information theory is a mathematical framework for quantifying information, communication, and data compression, foundational to modern digital communication and signal processing.
-
E.
Fano inequality
Fano inequality is a fundamental result in information theory that provides a lower bound on the probability of classification or decoding error in terms of conditional entropy.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elements of Information Theory Target entity description: Elements of Information Theory is a foundational textbook that systematically develops the theory and applications of information theory, widely used in communications, coding, and data science.
-
A.
Mathematical Foundations of Information Theory
Mathematical Foundations of Information Theory is a seminal monograph by Aleksandr Khinchin that rigorously develops the probabilistic and mathematical basis of Shannon’s information theory.
-
B.
An Introduction to Information Theory: Symbols, Signals and Noise
An Introduction to Information Theory: Symbols, Signals and Noise is a classic, accessible textbook that explains the fundamental concepts of information theory, communication, and coding for a broad scientific and engineering audience.
-
C.
Coding and Information Theory
"Coding and Information Theory" is a foundational textbook by Richard W. Hamming that introduces the mathematical principles underlying error-correcting codes and the transmission of information.
-
D.
information theory
Information theory is a mathematical framework for quantifying information, communication, and data compression, foundational to modern digital communication and signal processing.
-
E.
Fano inequality
Fano inequality is a fundamental result in information theory that provides a lower bound on the probability of classification or decoding error in terms of conditional entropy.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
non-fiction book
ⓘ
textbook ⓘ |
| author |
Joy A. Thomas
ⓘ
Thomas M. Cover ⓘ |
| countryOfPublication |
United States
ⓘ
linked to:
United States of America
|
| describedAs |
foundational textbook in information theory
ⓘ
standard reference for information theory courses ⓘ |
| field | information theory ⓘ |
| firstEditionPublicationYear | 1991 ⓘ |
| format |
ebook
ⓘ
hardcover ⓘ |
| hasEdition |
first edition
ⓘ
second edition ⓘ |
| hasInfluenceOn |
modern information theory textbooks
ⓘ
research in coding and communication theory ⓘ |
| language | English ⓘ |
| publisher |
Wiley
ⓘ
linked to:
John Wiley & Sons
|
| secondEditionPublicationYear | 2006 ⓘ |
| series | Wiley Series in Telecommunications and Signal Processing ⓘ |
| subject |
AEP
ⓘ
Fano inequality ⓘ Gaussian channels ⓘ Kolmogorov complexity ⓘ Markov chains ⓘ
linked to:
Markov processes
Shannon theory ⓘ
linked to:
information theory
channel capacity ⓘ channel coding ⓘ channel coding theorem ⓘ
linked to:
noisy-channel coding theorem
data compression ⓘ entropy ⓘ error exponents ⓘ hypothesis testing ⓘ mutual information ⓘ network information theory ⓘ rate-distortion theory ⓘ relative entropy ⓘ source coding ⓘ source coding theorem ⓘ typical sequences ⓘ universal coding ⓘ |
| targetAudience |
advanced undergraduates
ⓘ
graduate students ⓘ researchers ⓘ |
| usedIn |
coding theory
ⓘ
communications engineering ⓘ computer science education ⓘ data science education ⓘ electrical engineering education ⓘ signal processing education ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Elements of Information Theory Description of subject: Elements of Information Theory is a foundational textbook that systematically develops the theory and applications of information theory, widely used in communications, coding, and data science.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Elements of Information Theory
linked to: Elements of Information Theory
linked to: Elements of Information Theory