The shannon (symbol: Sh) is a unit of information named after Claude Shannon, the founder of information theory. IEC 80000-13 defines the shannon as the information content associated with an event when the probability of the event occurring is 1/2. It is understood as such within the realm of information theory, and is conceptually distinct from the bit, a term used in data processing and storage to denote a single instance of a binary signal. A sequence of n binary symbols (such as contained in computer memory or a binary data transmission) is properly described as consisting of n bits, but the information content of those n symbols may be less than n shannons according to the a priori probability of the actual sequence of symbols.
The shannon also serves as a unit of the information entropy of an event, which is defined as the expected value of the information content of the event (i.e., the probability-weighted average of all potential events). Given a number of possible outcomes, unlike information content, the entropy has an upper bound, which occurs when the possible outcomes are equiprobable. The maximum entropy of n bits is n Sh. A further quantity that it is used for is channel capacity (usually per unit time), which is the maximum of the expected value of the information content that can be transferred with negligible probability of error encoded over a channel.
Nevertheless, the term bits of information or simply bits is more often heard, even in the fields of information and communication theory, rather than shannons; just saying bits can therefore be ambiguous. Using the unit shannon is an explicit reference to a quantity of information content, information entropy or channel capacity, and is not restricted to binary data, whereas "bits" can as well refer to the number of binary symbols involved, as is the term used in fields such as data processing.
The shannon is connected through constants of proportionality to two other units of information:
The hartley, a seldom-used unit, is named after Ralph Hartley, an electronics engineer interested in the capacity of communications channels.
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Differential entropy (also referred to as continuous entropy) is a concept in information theory that began as an attempt by Claude Shannon to extend the idea of (Shannon) entropy, a measure of average (surprisal) of a random variable, to continuous probability distributions. Unfortunately, Shannon did not derive this formula, and rather just assumed it was the correct continuous analogue of discrete entropy, but it is not. The actual continuous version of discrete entropy is the limiting density of discrete points (LDDP).
The hartley (symbol Hart), also called a ban, or a dit (short for decimal digit), is a logarithmic unit that measures information or entropy, based on base 10 logarithms and powers of 10. One hartley is the information content of an event if the probability of that event occurring is . It is therefore equal to the information contained in one decimal digit (or dit), assuming a priori equiprobability of each possible value. It is named after Ralph Hartley.
In information theory, the information content, self-information, surprisal, or Shannon information is a basic quantity derived from the probability of a particular event occurring from a random variable. It can be thought of as an alternative way of expressing probability, much like odds or log-odds, but which has particular mathematical advantages in the setting of information theory. The Shannon information can be interpreted as quantifying the level of "surprise" of a particular outcome.
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EPFL2017
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