Linked from

The 64 pages that link to Information theory, each with the reason it gives.

• SemioticsCompared with: It measures signal transmission, unlike semiotics’ focus on signification and interpretation.

• Animal communicationRelated: Its concepts help quantify how much signals reduce uncertainty for receivers.

• Claude ShannonNarrower topic: Shannon founded this field by defining information quantitatively and deriving limits on communication.

• Error-correcting codeNarrower topic: Coding theory uses its limits on reliable communication through noisy channels.

• Bell LabsBroader topic: Claude Shannon developed its foundations at Bell Labs, giving communication systems a precise theory.

• Ludwig BoltzmannRelated: The shared logarithmic form of entropy links Boltzmann’s statistical ideas to later measures of information.

• CyberneticsRelated: Cybernetics uses information concepts to analyze signals and communication.

• BitNarrower topic: Information theory treats the bit as a standard unit for measuring information.

• Digital signal processingNarrower topic: It supplied a broader framework for understanding sampled signals and communication.

• UncertaintyNarrower topic: Its measures of entropy quantify uncertainty across possible outcomes.

• Computer scienceRelated: It quantifies information and explains limits on storage and transmission.

• Andrey KolmogorovNarrower topic: Kolmogorov’s complexity offered a complementary, object-by-object account of information.

• Lossless compressionNarrower topic: It provides the framework for measuring how compactly a source can be encoded.

• Norbert WienerCompared with: Shannon’s theory formalized communication differently, despite close historical overlap with Wiener’s signal work.

• Signal modulationNarrower topic: It provides measures for how much information a modulated signal can convey.

• CryptanalysisRelated: Its measures of uncertainty help describe what ciphertext reveals about hidden messages.

• Signal processingCompared with: It measures information limits, while signal processing develops operations on actual signal representations.

• Hamming distanceNarrower topic: Hamming distance became a practical measure of symbol errors within this broader field.

• CommunicationCompared with: It measures signal transmission without claiming to explain meaning itself.

• Huffman codingNarrower topic: Huffman coding is a practical source-coding method within this broader theory.

• Information economicsCompared with: It measures communication and uncertainty mathematically, rather than analyzing their market incentives.

• Communication channelNarrower topic: It supplies the formal framework for measuring communication over channels.

• Digital communicationNarrower topic: It provides a broader framework for limits on representing and transmitting information.

• Digital mediaNarrower topic: Its concepts explain the limits and tradeoffs behind storing and transmitting digital media.

• Quantum information scienceNarrower topic: It provides concepts such as entropy, channel capacity, and coding for quantum extensions.

• Coding theoryNarrower topic: Its entropy and channel-capacity results constrain what coding systems can achieve.

• Shannon–Hartley theoremNarrower topic: The theorem is a central quantitative result within this broader theory.

• TelecommunicationsNarrower topic: Its capacity limits and coding principles explain what communication channels can achieve.

• A Mathematical Theory of CommunicationNarrower topic: Shannon’s paper established the field by quantifying information and proving communication limits.

• Computational neuroscienceRelated: It quantifies how much information neural responses carry about stimuli.

• Harry NyquistNarrower topic: Nyquist’s transmission limits were precursors to the field Shannon formalized.

• One-time padNarrower topic: Its formal tools define the secrecy guarantee and the randomness a pad requires.

• Information scienceRelated: Its measures of information and communication provide a formal foundation for the field.

• Low-density parity-check codeNarrower topic: LDPC codes arose from the problem of reliable communication at high rates.

• Ralph HartleyNarrower topic: Hartley’s work supplied an early quantitative foundation for this field.

• Binary logarithmNarrower topic: Binary logarithms measure information in bits when outcomes are equally likely.

• Minimum description lengthNarrower topic: MDL draws on information theory’s link between code length and probability.

• Monty Hall problemRelated: The host’s constrained reveal conveys information about the hidden prize location.

• RedundancyNarrower topic: Its measures distinguish useful information from predictable, redundant structure.

• Zellig HarrisRelated: Harris explored statistical relations between linguistic structure and information.

• Communication studiesCompared with: Its formal account of signals differs from communication studies’ attention to meaning, context, and social effects.

• Communication theoryRelated: It supplies measures of information and formal limits for communication.

• Computational linguisticsRelated: Its probabilistic ideas informed early quantitative approaches to language and communication.

• Hamming codeNarrower topic: Hamming codes address reliable communication under the errors studied by this field.

• Dennis GaborRelated: Gabor applied information-theoretic ideas to communication and the limits of human hearing.

• Discrete mathematicsRelated: Its measures and coding problems often concern finite alphabets and discrete messages.

• Formal scienceRelated: It uses formal quantities and proofs to analyze communication and information.

• Information AgeRelated: Its tools helped engineers design reliable digital communication systems.

• History of probabilityRelated: Probability became central to quantifying uncertainty in messages and communication channels.

• Brain teaserRelated: Clue design can be understood in terms of how much uncertainty each hint removes.