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.

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 contained in each message, taken over all possibilities for the transmitted message.

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

2023年4月19日 · Entropy is a measure of the uncertainty or randomness of a probability distribution. In the context of information theory, it represents the average amount of information required to...

2020年7月13日 · A cornerstone of information theory is the idea of quantifying how much information there is in a message. More generally, this can be used to quantify the information in an event and a random variable, called entropy, and is calculated using probability.

Entropy is simply the average(expected) amount of the information from the event. How was the entropy equation is derived? So when you look at the difference between the total Information from N occurrences and the Entropy equation, only thing that changed in the place of N. The N is moved to the right, which means that I/N is Entropy.

How the formal concepts of information are grounded in the principles and rules of probability. Entropies Defined, and Why They Are Measures of Information. Marginal entropy, joint entropy, conditional entropy, and the Chain Rule for entropy. Mutual information between ensembles of random variables.

2024年6月18日 · Entropy measures the uncertainty or unpredictability of a random variable. In machine learning, entropy quantifies the amount of information required to describe a dataset. Interpretation: Higher entropy indicates greater unpredictability, while lower entropy indicates more predictability. 2. Mutual Information.

2025年1月2日 · Shannon’s concept of entropy can now be taken up. Recall that the table Comparison of two encodings from M to S showed that the second encoding scheme would transmit an average of 5.7 characters from M per second. But suppose that, instead of the distribution of characters shown in the table, a long series of As were transmitted.

2022年9月27日 · Examples of Information Gain and Entropy. To understand the concept of Shannon entropy, consider a coin flip. For a fair coin, where heads and tails are equally likely, the entropy is maximal, as the uncertainty before observing the outcome is the highest: