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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
Shannon's definition of entropy, when applied to an information source, can determine the minimum channel capacity required to reliably transmit the source as encoded binary digits. Shannon's entropy measures the information contained in a message as opposed to the portion of the message that is determined (or predictable).
Shannon entropy, also called information entropy, was proposed by Shannon in 1948 (Shannon, 1948). According to the definition of entropy, Shannon entropy is used to measure data uncertainty. The Shannon's entropy method was used to determine weight values of the criteria (Sengul, Eren, & Shiraz, 2015; Wu et al., 2018 a) in this paper.
Shannon’s entropy quantifies the amount of information in a variable, thus providing the foundation for a theory around the notion of information. Storage and transmission of information can intuitively be expected to be tied to the amount of information involved.
Shannon’s discovery of the fundamental laws of data compression and transmission marks the birth of Information Theory. In this note, we first discuss how to formulate the main fundamental quantities in In-formation Theory: information, Shannon entropy and channel capacity.
From a physical point of view, information theory has nothing to do with physics. However, the concept of Shanon entropy shares some intuition with Boltzmann’s, and some of the mathematics developed in information theory turns out to have relevance in statistical mechanics.
1 Ιουλ 2023 · Shannon entropy: intuitive construction, definition and properties. To understand the concepts of entropy and quantity of information, consider the following example.
