Slepian–Wolf coding theorem
E641830
The Slepian–Wolf coding theorem is a fundamental result in information theory that characterizes the limits of lossless data compression for correlated sources encoded separately but decoded jointly.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Slepian–Wolf bound | 1 |
| Slepian–Wolf coding theorem canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7115758 — 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: Slepian–Wolf coding theorem Context triple: [David Slepian, notableWork, Slepian–Wolf coding theorem]
-
A.
Wozencraft ensemble in coding theory
The Wozencraft ensemble in coding theory is a family of randomly constructed linear codes introduced by John Wozencraft that plays a key role in analyzing the performance limits of coding schemes, particularly for achieving capacity on noisy channels.
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B.
noisy-channel coding theorem
The noisy-channel coding theorem is a fundamental result in information theory that establishes the maximum rate at which information can be transmitted over a noisy communication channel with arbitrarily low error using appropriate encoding schemes.
-
C.
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.
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D.
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.
-
E.
source coding theorem
The source coding theorem is a fundamental result in information theory that establishes the minimum average number of bits needed to losslessly encode symbols from a given information source, linking this limit to the source’s 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: Slepian–Wolf coding theorem Target entity description: The Slepian–Wolf coding theorem is a fundamental result in information theory that characterizes the limits of lossless data compression for correlated sources encoded separately but decoded jointly.
-
A.
Wozencraft ensemble in coding theory
The Wozencraft ensemble in coding theory is a family of randomly constructed linear codes introduced by John Wozencraft that plays a key role in analyzing the performance limits of coding schemes, particularly for achieving capacity on noisy channels.
-
B.
noisy-channel coding theorem
The noisy-channel coding theorem is a fundamental result in information theory that establishes the maximum rate at which information can be transmitted over a noisy communication channel with arbitrarily low error using appropriate encoding schemes.
-
C.
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.
-
D.
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.
-
E.
source coding theorem
The source coding theorem is a fundamental result in information theory that establishes the minimum average number of bits needed to losslessly encode symbols from a given information source, linking this limit to the source’s entropy.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
coding theorem
ⓘ
information theory theorem ⓘ |
| appliesTo |
discrete memoryless sources
ⓘ
stationary ergodic sources (with extensions) ⓘ |
| assumes |
joint decoder
ⓘ
known joint distribution of sources at encoder and decoder (in classical formulation) ⓘ separate encoders ⓘ two or more correlated discrete memoryless sources ⓘ |
| characterizes | rate region for lossless compression of correlated sources ⓘ |
| concerns |
correlated information sources
ⓘ
distributed source coding ⓘ lossless data compression ⓘ separate encoding and joint decoding ⓘ |
| field | information theory ⓘ |
| generalizationOf | lossless source coding to distributed encoders ⓘ |
| guarantees | arbitrarily small probability of decoding error for rates in achievable region ⓘ |
| hasApplicationIn |
Wyner–Ziv coding
NERFINISHED
ⓘ
compressing correlated data streams ⓘ distributed sensor networks ⓘ multiterminal source coding ⓘ network information theory ⓘ |
| hasCodingApproach |
LDPC code based Slepian–Wolf coding
ⓘ
syndrome-based coding using linear channel codes ⓘ turbo code based Slepian–Wolf coding ⓘ |
| implies |
correlation can be exploited at the decoder
ⓘ
no rate loss compared to joint encoding for lossless compression ⓘ |
| influenced |
correlation-aware compression algorithms
ⓘ
development of distributed video coding ⓘ |
| inspired | practical Slepian–Wolf codes based on channel codes ⓘ |
| introducedIn | 1973 ⓘ |
| involves | asymptotically long block lengths ⓘ |
| isSpecialCaseOf | multiterminal source coding theory ⓘ |
| namedAfter |
David Slepian
NERFINISHED
ⓘ
Jack Wolf NERFINISHED ⓘ |
| publishedIn | IEEE Transactions on Information Theory NERFINISHED ⓘ |
| rateConstraint |
R_X + R_Y ≥ H(X,Y)
ⓘ
R_X ≥ H(X|Y) ⓘ R_Y ≥ H(Y|X) ⓘ |
| relatedTo |
Shannon source coding theorem
NERFINISHED
ⓘ
Wyner–Ziv theorem NERFINISHED ⓘ network coding ⓘ |
| shows | side information at the decoder is sufficient for optimal compression rates ⓘ |
| states |
each individual rate must be at least conditional entropy given the other source
ⓘ
sum of individual rates must be at least joint entropy of sources ⓘ |
| usesConcept |
conditional entropy
ⓘ
entropy ⓘ joint entropy ⓘ joint typicality decoding ⓘ typical sequences ⓘ |
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: Slepian–Wolf coding theorem Description of subject: The Slepian–Wolf coding theorem is a fundamental result in information theory that characterizes the limits of lossless data compression for correlated sources encoded separately but decoded jointly.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Slepian–Wolf bound