Search results
Results From The WOW.Com Content Network
In this context, either an information-theoretical measure, such as functional clusters (Gerald Edelman and Giulio Tononi's functional clustering model and dynamic core hypothesis (DCH) [47]) or effective information (Tononi's integrated information theory (IIT) of consciousness [48] [49] [50]), is defined (on the basis of a reentrant process ...
Some of the oldest methods of telecommunications implicitly use many of the ideas that would later be quantified in information theory. Modern telegraphy, starting in the 1830s, used Morse code, in which more common letters (like "E", which is expressed as one "dot") are transmitted more quickly than less common letters (like "J", which is expressed by one "dot" followed by three "dashes").
2003 – David J. C. MacKay shows the connection between information theory, inference and machine learning in his book. 2006 – Jarosław Duda introduces first Asymmetric numeral systems entropy coding: since 2014 popular replacement of Huffman and arithmetic coding in compressors like Facebook Zstandard , Apple LZFSE , CRAM or JPEG XL
Many of the concepts in information theory have separate definitions and formulas for continuous and discrete cases. For example, entropy is usually defined for discrete random variables, whereas for continuous random variables the related concept of differential entropy, written (), is used (see Cover and Thomas, 2006, chapter 8).
Phi; the symbol used for integrated information. Integrated information theory (IIT) proposes a mathematical model for the consciousness of a system. It comprises a framework ultimately intended to explain why some physical systems (such as human brains) are conscious, [1] and to be capable of providing a concrete inference about whether any physical system is conscious, to what degree, and ...
Articles relating to information theory, which studies the quantification, storage, and communication of information. Subcategories. This category has the following ...
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. This measures the expected amount of information needed to describe the state of the variable, considering the distribution of probabilities across all potential ...
information bottleneck method; information theoretic security; information theory; joint entropy; Kullback–Leibler divergence; lossless compression; negentropy; noisy-channel coding theorem (Shannon's theorem) principle of maximum entropy; quantum information science; range encoding; redundancy (information theory) Rényi entropy; self ...