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Coding theory

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Abstract Coding theory is a portion of information theory concerned with the explicit representation of data as a sequence of symbols, usually a sequence of bits. The entropy of a probability distribution measures the information content of data, giving a lower bound on the number of bits necessary to encode data. Source coding also defines a measure, the divergence, of the cost of using a suboptimal representation. Channel coding describes representations that maximize the rate at which information can be communicated through a noisy medium. Important applications of coding theory include data compression, signal processing, and the comparison of statistical models. Copyright © 2009 Wiley Periodicals, Inc. This article is categorized under: Applications of Computational Statistics > Signal and Image Processing and Coding

Arithmetic coding recursively identifies a location in the cumulative distribution.

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Binary symmetric channel.

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Arithmetic coding uses the position of a string in the cumulative distribution of five‐character messages to encode efficiently the string as a sequence of bits.

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Binary entropy function.

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Applications of Computational Statistics > Signal and Image Processing and Coding

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