Linked from

The 26 pages that link to Shannon entropy, each with the reason it gives.

• Information theoryBroader topic: It is the central measure of information in Shannon’s framework.

• LogarithmRelated: Logarithms quantify information in additive units across independent events.

• Claude ShannonBroader topic: This measure gives Shannon’s precise meaning to the information produced by uncertain outcomes.

• BitRelated: Entropy quantifies how much information a bit-valued source carries on average.

• Lossless compressionRelated: It gives a lower bound on average code length for ideal lossless coding.

• Natural logarithmRelated: Using natural logarithms expresses entropy in nats rather than bits.

• Kolmogorov complexityCompared with: Entropy is an ensemble average, whereas Kolmogorov complexity describes one individual object.

• Indus scriptRelated: Entropy comparisons have been used to assess whether Indus sign sequences show linguistic-like structure.

• Compression ratioNarrower topic: Entropy bounds ideal coding lengths, helping explain limits on achievable compression.

• Huffman codingRelated: Entropy provides a lower bound on the average length of lossless codes.

• Fisher informationCompared with: Entropy measures outcome uncertainty, while Fisher information measures sensitivity to a parameter.

• Shannon–Hartley theoremRelated: The theorem's data-rate limit is grounded in Shannon's quantitative theory of information.

• Landauer's principleRelated: The information lost during erasure determines the minimum entropy transferred to the surroundings.

• Source coding theoremRelated: For lossless coding, entropy supplies the minimum average rate.

• MajorizationRelated: For probability vectors, majorization orders entropy: more spread-out vectors have greater Shannon entropy.

• Algorithmic information theoryCompared with: It measures ensemble uncertainty, unlike Kolmogorov complexity's description of individual objects.

• Binary logarithmRelated: Its formula uses log₂ when uncertainty is measured in bits.

• Shannon's source coding theoremRelated: It sets the asymptotic minimum number of bits required per source symbol.

• Boltzmann's entropy formulaRelated: Its logarithmic form parallels the information content of the state count.

• Karamata's inequalityBroader topic: The concavity of x log x reverses the majorization comparison and orders entropy.

• Gibbs' inequalityRelated: Gibbs' inequality implies that cross-entropy cannot fall below this entropy.

• Heaps's lawCompared with: Entropy summarizes distributional uncertainty, not the count of distinct words as text grows.

• Infinite monkey theoremRelated: Character probabilities determine how rapidly target strings become rare.

• Information and communication theoryRelated: It quantifies the information produced by a source and underlies source-coding limits.

• Lochs's theoremRelated: The decimal and continued-fraction codings have different information rates, whose ratio governs the count.

• Noisy-channel coding theoremRelated: Entropy quantifies uncertainty in channel inputs and outputs.