Exemplos de uso de Kolmogorov complexity em Inglês e suas traduções para o Português
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Kolmogorov complexity is not computable.
This is an extension of Kolmogorov complexity.
Using the Kolmogorov complexity gives an unbiased estimate(a universal prior) of the prior probability of a number.
It can be proven that the Kolmogorov complexity is not computable.
Fundamental ingredients of the theory are the concepts of algorithmic probability and Kolmogorov complexity.
The problem of determining the Kolmogorov complexity of a string.
Kolmogorov complexity became not only a subject of independent study but is also applied to other subjects as a tool for obtaining proofs.
For that reason,constant terms tend to be disregarded in Kolmogorov complexity theory.
From the standpoint of Kolmogorov complexity theory, this calculation is problematic.
The length of the shortest program that outputs the data is called the Kolmogorov complexity of the data.
Li and Vitanyi's book An Introduction to Kolmogorov Complexity and Its Applications is the standard introduction to these ideas.
Unlike the definition of randomnessfor a finite string, is not affected by which universal machine is used to define prefix-free Kolmogorov complexity.
Given a pattern"T",the number of other patterns may have Kolmogorov complexity no larger than that of"T" is denoted by φ"T.
It differs from Kolmogorov complexity in that it considers the computation time of the algorithm with the shortest length, rather than its length.
Precise definitions==A binary string is said to be random if the Kolmogorov complexity of the string is at least the length of the string.
The Kolmogorov complexity is defined using formal languages, or Turing machines which avoids ambiguities about which string results from a given description.
The minimum description length(MDL) principle has been developed from ideas in information theory and the theory of Kolmogorov complexity.
However, proponents argue that in terms of Kolmogorov complexity the proposed multiverse is simpler than a single idiosyncratic universe.
Kolmogorov complexity provides a measure of the computational resources needed to specify a pattern such as a DNA sequence or a sequence of alphabetic characters.
Concrete examples of such functions are Busy beaver, Kolmogorov complexity, or any function that outputs the digits of a noncomputable number, such as Chaitin's constant.
Kolmogorov complexity(Schnorr 1973, Levin 1973): Kolmogorov complexity can be thought of as a lower bound on the algorithmic compressibility of a finite sequence of characters or binary digits.
The proof by contradiction shows that if it were possible to compute the Kolmogorov complexity, then it would also be possible to systematically generate paradoxes similar to this one, i.e.
Relationship with Kolmogorov complexity==It is not possible in general to unambiguously define what is the minimal number of symbols required to describe a given string given a specific description mechanism.
Chaitin's incompleteness theorem states that for any theory that can represent enough arithmetic,there is an upper bound"c" such that no specific number can be proven in that theory to have Kolmogorov complexity greater than"c.
There are several variants of Kolmogorov complexity or algorithmic information; the most widely used one is based on self-delimiting programs and is mainly due to Leonid Levin 1974.
Chaitin's theorem states that for any theory that can represent enough arithmetic,there is an upper bound"c" such that no specific number can be proven in that theory to have Kolmogorov complexity greater than"c.
In algorithmic information theory,the notion of Kolmogorov complexity is named after the famous mathematician Andrey Kolmogorov even though it was independently discovered and published by Ray Solomonoff a year before Kolmogorov. .
Since Kolmogorov complexity depends on a fixed choice of universal Turing machine(informally, a fixed"description language" in which the"descriptions" are given), the collection of random strings does depend on the choice of fixed universal machine.
The proof by contradiction shows that if it were possible to compute the Kolmogorov complexity, then it would also be possible to systematically generate paradoxes similar to this one, i.e. descriptions shorter than what the complexity of the described string implies.
Also, since it can be shown that the Kolmogorov complexity relative to two different universal machines differs by at most a constant, the collection of random infinite sequences does not depend on the choice of universal machine in contrast to finite strings.