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  1. en.wikipedia.org › wiki › Entropy_(information_theory)Entropy (information theory) - Wikipedia

    Generally, information entropy is the average amount of information conveyed by an event, when considering all possible outcomes. The concept of information entropy was introduced by Claude Shannon in his 1948 paper "A Mathematical Theory of Communication", and is also referred to as Shannon entropy.

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  3. machinelearningmastery.com › what-is-informationA Gentle Introduction to Information Entropy ...

    Jul 13, 2020 · Information entropy is a measure of the average amount of information needed to represent an event drawn from a probability distribution for a random variable. Learn how to calculate information entropy using logarithms and probability, and see examples of how it is used in machine learning and data compression.

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  5. brilliant.org › wiki › entropy-information-theoryEntropy (Information Theory) | Brilliant Math & Science Wiki

    Entropy is a measure of how much information a message contains, based on the probabilities of its possible outcomes. Learn the formal definition, properties, and examples of entropy in information theory.

  6. ee.stanford.edu › ~gray › itEntropy and Information Theory - Stanford University

    notions of the information in random variables, random processes, and dynam-ical systems. Examples are entropy, mutual information, conditional entropy, conditional information, and relative entropy (discrimination, Kullback-Leibler information), along with the limiting normalized versions of these quantities such as entropy rate and ...

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  7. arxiv.org › pdf › 1612A Brief Introduction to Shannon’s Information Theory

    1. 2 Information and Entropy. What is information? or, what does it mean when Michael says he has gotten some information regarding something? Well, it means that he did not know what this “something” is about before someone else “communicate” some stuff about this “something” to him. But now, after the com- munication, he knows it.

    • Ricky X. F. Chen
    • 2016

  8. web.stanford.edu › ~montanar › RESEARCHINTRODUCTION TO INFORMATION THEORY - Stanford University

    Intuitively, the entropy gives a measure of the uncertainty of the random variable. It is sometimes called the missing information: the larger the entropy, the less a priori information one has on the value of the random variable. This measure is roughly speaking the logarithm of the number of typical values that

  9. www.khanacademy.org › v › information-entropyInformation entropy (video) | Khan Academy

    The fundamental idea is that, if the entropy of an information source drops, that means we can ask fewer questions to guess the outcome. Thanks to Shannon, the bit, which is the unit of entropy, is adopted as our quantitative measure of information, or measure of surprise.

    • 7 min
    • Brit Cruise

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