In information theory, the entropy of a random variable quantifies the average level of uncertainty or information associated with the variable's potential states or possible outcomes.

Jul 13, 2020 · More generally, this can be used to quantify the information in an event and a random variable, called entropy, and is calculated using probability.

Feb 21, 2025 · Information is tied to the field of probabilities, and it can be seen as a measure of uncertainty. To avoid extrapolation and misuse of this concept, you need to remember that it …

Nov 20, 2025 · Information entropy measures the uncertainty in data by quantifying the average amount of information produced by a random variable. Developed by Claude Shannon, it …

Jun 23, 2025 · Entropy quantifies the amount of "information" contained in a message or system, and is foundational in diverse domains such as data compression, cryptography, statistical …

Nov 26, 2025 · Borrowed from physics but reimagined for communication, entropy measures how unpredictable an event or message is. The more surprising an outcome, the more …

Dec 5, 2025 · This is the quantity that he called entropy, and it is represented by H in the following formula: H = p1 log s (1/ p1) + p2 log s (1/ p2) + ⋯ + pk log s (1/ pk). (For a review of logs, see …

Jun 12, 2025 · Entropy is a fundamental concept in information theory that measures the amount of uncertainty or randomness in a system. In the context of information theory, entropy is used …

May 15, 2025 · Information entropy is a fundamental concept in information theory introduced by Claude Shannon in 1948. It quantifies the uncertainty or unpredictability associated with …

In essence, the "information content" can be viewed as how much useful information the message actually contains. The entropy, in this context, is the expected number of bits of information …