Algorithmic information theory is a subfield of information theory and computer science that concerns itself with the relationship between computation and information. According to Gregory Chaitin, it is "the result of putting Shannon's information theory and Turing's computability theory into a cocktail shaker and shaking vigorously."
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... July 25, 1926 – December 7, 2009) was the inventor of algorithmic probability, and founder of algorithmic information theory, He was an originator of the branch of artificial ... Solomonoff first described algorithmic probability in 1960, publishing the crucial theorem that launched Kolmogorov complexity and algorithmic information theory. 1960, "A Preliminary Report on a General Theory of Inductive Inference." He clarified these ideas more fully in his 1964 publications, "A Formal Theory of Inductive Inference," Part I and Part II ...
... Algorithmic information theory (AIT) is the information theory of individual objects, using computer science, and concerns itself with the relationship between computation, information, and randomness ... The information content or complexity of an object can be measured by the length of its shortest description ... For instance the string More formally, the Algorithmic Complexity (AC) of a string x is defined as the length of the shortest program that computes or outputs x, where the program is run ...
... Main article Algorithmically random sequence See also Kolmogorov complexity and Algorithmic information theory In 1964–65 Martin-Löf studied in Moscow under the supervision of ... Martin-Löf's key insight was to use the theory of computation to define formally the notion of a test for randomness ... This contrasts with the idea of randomness in probability in that theory, no particular element of a sample space can be said to be random ...
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