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Information theory. In information theory, the entropy of a random variable is the average level of "information", "surprise", or "uncertainty" inherent to the variable's possible outcomes. Given a discrete random variable , which takes values in the set and is distributed according to , the entropy is.
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.
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.
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.
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.
- Robert M. Gray
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.
20. Nov. 2021 · Information theory studies sequences of letters or symbols from a finite alphabet. It presumes that a source produces these letters with a given probability distribution and then studies how information is degraded when storing, processing, or transmitting data.
