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

   en.wikipedia.org › wiki › Entropy_(information_theory)

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   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.

2. de.wikipedia.org › wiki › Entropie_(Informationstheorie)Entropie (Informationstheorie) – Wikipedia

   de.wikipedia.org › wiki › Entropie_(Informationstheorie)

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   1 Definition. 2 Interpretation. 3 Maximaler Entropiewert und Normierung. 4 Beispiele. 4.1 Alphabet. 4.2 Münzwurf. 4.3 Idealer Würfel. 5 Entropietests. 6 Datenkompression und Entropie. 7 Alternative Möglichkeiten der Informationsquantifizierung. 8 Ähnlichkeit zur Entropie in der Physik. 9 Siehe auch. 10 Literatur. 11 Weblinks. 12 Einzelnachweise.

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

   ee.stanford.edu › ~gray › it

   A comprehensive textbook on the foundations and applications of entropy and information theory. Covers topics such as probability spaces, random processes, distributions, ergodic properties, relative entropy, information rates, and ergodic theorems.

   • Dateigröße: 1MB

   • Seitenanzahl: 311

5. machinelearningmastery.com › what-is-information-entropyA Gentle Introduction to Information Entropy

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   13. Juli 2020 · Learn how to quantify the amount of information in an event or a random variable using probability and logarithms. This tutorial covers the basics of information theory, information, and entropy, and their applications in machine learning.

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

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   Learn how to measure the information content and uncertainty of a message in information theory. Find the formal definitions, properties, and examples of entropy and information for random variables.

7. link.springer.com › book › 10Entropy and Information Theory | SpringerLink

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   A classic text on information theory and its applications to communication systems, with new material on stationary/sliding-block codes, ergodic theory, and rate-distortion theory. The book covers sources, channels, codes, and information and distortion measures and their properties.

8. www.pnas.org › doi › fullInformation theory: A foundation for complexity science | PNAS

   www.pnas.org › doi › full

   Information theory (in particular, the maximum information entropy formalism) provides a way to deal with such complexity. It has been applied to numerous problems, within and across many disciplines, over the last few decades.