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Jump to navigationJump to searchNew page: '''Information theory''' is a discipline in applied mathematics involving the quantification of data with the goal of enabling as much data as possible to be reliably stored on a medi...
'''Information theory''' is a discipline in [[applied mathematics]] involving the quantification of data with the goal of enabling as much data as possible to be reliably stored on a medium or communicated over a channel. The measure of information, known as [[information entropy]], is usually expressed by the average number of bits needed for storage or communication. For example, if a daily weather description has an entropy of 3 bits, then, over enough days, we can describe daily weather with an ''average'' of approximately 3 bits per day.
Applications of fundamental topics of information theory include [[lossless data compression]] (e.g. [[ZIP (file format)|ZIP files]]), [[lossy data compression]] (e.g. [[MP3]]s), and [[channel capacity|channel coding]] (e.g. for [[DSL]] lines). The field is at the crossroads of [[mathematics]], [[statistics]], [[computer science]], [[physics]], [[neurobiology]], and [[electrical engineering]]. Its impact has been crucial to success of the [[Voyager program|Voyager]] missions to deep space, the invention of the [[Compact disc|CD]], the feasibility of mobile phones, the development of the [[Internet]], the study of [[linguistics]] and of human perception, the understanding of [[black hole]]s, and numerous other fields.
== Overview ==
The main concepts of information theory can be grasped by considering the most widespread means of human communication: language. Two important aspects of a good language are as follows: First, the most common words (e.g., "a," "the," "I") should be shorter than less common words (e.g., "benefit," "generation," "mediocre"), so that sentences will not be too long. Such a tradeoff in word length is analogous to [[data compression]] and is the essential aspect of [[source coding]]. Second, if part of a sentence is unheard or misheard due to noise—e.g., a passing car—the listener should still be able to glean the meaning of the underlying message. Such robustness is as essential for an electronic communication system as it is for a language; properly building such robustness into communications is done by [[Channel capacity|channel coding]]. Source coding and channel coding are the fundamental concerns of information theory.
Note that these concerns have nothing to do with the ''importance'' of messages. For example, a platitude such as "Thank you; come again" takes about as long to say or write as the urgent plea, "Call an ambulance!" while clearly the latter is more important and more meaningful. Information theory, however, does not involve message importance or meaning, as these are matters of the quality of data rather than the quantity of data, the latter of which is determined solely by probabilities.
Information theory is generally considered to have been founded in 1948 by [[Claude Elwood Shannon|Claude Shannon]] in his seminal work, "[[A Mathematical Theory of Communication]]." The central paradigm of classical information theory is the engineering problem of the transmission of information over a noisy channel. The most fundamental results of this theory are Shannon's [[source coding theorem]], which establishes that, on average, the number of ''bits'' needed to represent the result of an uncertain event is given by its [[information entropy|entropy]]; and Shannon's [[noisy-channel coding theorem]], which states that ''reliable'' communication is possible over ''noisy'' channels provided that the rate of communication is below a certain threshold called the channel capacity. The channel capacity can be approached by using appropriate encoding and decoding systems.
Information theory is closely associated with a collection of pure and applied disciplines that have been investigated and reduced to engineering practice under a variety of rubrics throughout the world over the past half century or more: [[adaptive system]]s, [[anticipatory system]]s, [[artificial intelligence]], [[complex system]]s, [[complexity science]], [[cybernetics]], [[informatics]], [[machine learning]], along with [[systems science]]s of many descriptions. Information theory is a broad and deep mathematical theory, with equally broad and deep applications, amongst which is the vital field of [[coding theory]].
Coding theory is concerned with finding explicit methods, called ''codes'', of increasing the efficiency and reducing the net error rate of data communication over a noisy channel to near the limit that Shannon proved is the maximum possible for that channel. These codes can be roughly subdivided into [[data compression]] (source coding) and [[error-correction]] (channel coding) techniques. In the latter case, it took many years to find the methods Shannon's work proved were possible. A third class of information theory codes are cryptographic algorithms (both [[code (cryptography)|code]]s and [[cipher]]s). Concepts, methods and results from coding theory and information theory are widely used in [[cryptography]] and [[cryptanalysis]]. ''See the article [[ban (information)]] for a historical application.''
Information theory is also used in [[information retrieval]], [[intelligence (information gathering)|intelligence gathering]], [[gambling]], [[statistics]], and even in [[musical composition]].
==Historical background==
{{main|History of information theory}}
The landmark event that established the discipline of information theory, and brought it to immediate worldwide attention, was the publication of [[Claude E. Shannon]]'s classic paper "[[A Mathematical Theory of Communication]]" in the ''[[Bell System Technical Journal]]'' in July and October of 1948.
Prior to this paper, limited information theoretic ideas had been developed at Bell Labs, all implicitly assuming events of equal probability. [[Harry Nyquist]]'s 1924 paper, ''Certain Factors Affecting Telegraph Speed,'' contains a theoretical section quantifying "intelligence" and the "line speed" at which it can be transmitted by a communication system, giving the relation <math>W = K \log m</math>, where ''W'' is the speed of transmission of intelligence, ''m'' is the number of different voltage levels to choose from at each time step, and ''K'' is a constant. [[Ralph Hartley]]'s 1928 paper, ''Transmission of Information,'' uses the word ''information'' as a measurable quantity, reflecting the receiver's ability to distinguish that one sequence of symbols from any other, thus quantifying information as <math>H = \log S^n = n \log S</math>, where ''S'' was the number of possible symbols, and ''n'' the number of symbols in a transmission. The natural unit of information was therefore the decimal digit, much later renamed the [[ban (information)|hartley]] in his honour as a unit or scale or measure of information. [[Alan Turing]] in 1940 used similar ideas as part of the statistical analysis of the breaking of the German second world war [[Cryptanalysis of the Enigma|Enigma]] ciphers.
Much of the mathematics behind information theory with events of different probabilities was developed for the field of [[thermodynamics]] by [[Ludwig Boltzmann]] and [[J. Willard Gibbs]]. Connections between information-theoretic entropy and thermodynamic entropy, including the important contributions by [[Rolf Landauer]] in the 1960s, are explored in ''[[Entropy in thermodynamics and information theory]]''.
In Shannon's revolutionary and groundbreaking paper, the work for which had been substantially completed at Bell Labs by the end of 1944, Shannon for the first time introduced the qualitative and quantitative model of communication as a statistical process underlying information theory, opening with the assertion that
:"The fundamental problem of communication is that of reproducing at one point, either exactly or approximately, a message selected at another point."
With it came the ideas of
* the [[information entropy]] and [[redundancy (information theory)|redundancy]] of a source, and its relevance through the [[source coding theorem]];
* the [[mutual information]], and the [[channel capacity]] of a noisy channel, including the promise of perfect loss-free communication given by the [[noisy-channel coding theorem]];
* the practical result of the [[Shannon–Hartley law]] for the channel capacity of a Gaussian channel; and of course
* the [[bit]]—a new way of seeing the most fundamental unit of information
==Quantities of information==
Information theory is based on [[probability theory]] and [[statistics]]. The most important quantities of information are [[Information entropy|entropy]], the information in a [[random variable]], and [[mutual information]], the amount of information in common between two random variables. The former quantity indicates how easily message data can be [[data compression|compressed]] while the latter can be used to find the communication rate across a [[Channel (communications)|channel]].
[[Category: General Reference]]
