Results 11 to 20 of about 1,307,610 (290)
A Generalized Characterization of Algorithmic Probability [PDF]
Theory of Computing Systems, 2017 An a priori semimeasure (also known as "algorithmic probability" or "the Solomonoff prior" in the context of inductive inference) is defined as the transformation, by a given universal monotone Turing machine, of the uniform measure on the infinite strings. It is shown in this paper that the class of a priori semimeasures can equivalently be defined as
Sterkenburg, Tom F. +3 more
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Stationary algorithmic probability [PDF]
Theoretical Computer Science, 2010 Kolmogorov complexity and algorithmic probability are defined only up to an additive resp. multiplicative constant, since their actual values depend on the choice of the universal reference computer. In this paper, we analyze a natural approach to eliminate this machine-dependence.
Müller, Markus
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Diverse Consequences of Algorithmic Probability [PDF]
, 2013 We reminisce and discuss applications of algorithmic probability to a wide range of problems in artificial intelligence, philosophy and technological society. We propose that Solomonoff has effectively axiomatized the field of artificial intelligence, therefore establishing it as a rigorous scientific discipline.
Özkural, Eray, Eray Özkural
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Notions and applications of algorithmic randomness [PDF]
, 2013 Algorithmic randomness uses computability theory to define notions of randomness for infinite objects such as infinite binary sequences. The different possible definitions lead to a hierarchy of randomness notions. In this thesis we study this hierarchy,
Vermeeren, Stijn
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A Physical Framework for Algorithmic Entropy
EntropyThis paper does not aim to prove new mathematical theorems or claim a fundamental unification of physics and information, but rather to provide a new pedagogical framework for interpreting foundational results in algorithmic information theory. Our focus
Jeff Edmonds
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Two-dimensional Kolmogorov complexity and an empirical validation of the Coding theorem method by compressibility [PDF]
PeerJ Computer Science, 2015 We propose a measure based upon the fundamental theoretical concept in algorithmic information theory that provides a natural approach to the problem of evaluating n-dimensional complexity by using an n-dimensional deterministic Turing machine.
Hector Zenil +3 more
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Ex-Post Algorithmic Probability
, 2011 Algorithmic probability is traditionally defined by considering the output of a universal machine fed with random programs. This definition proves inappropriate for many practical applications where probabilistic assessments are spontaneously and ...
Dessalles, Jean-Louis
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Quantum Probability as an Application of Data Compression Principles [PDF]
Electronic Proceedings in Theoretical Computer Science, 2016 Realist, no-collapse interpretations of quantum mechanics, such as Everett's, face the probability problem: how to justify the norm-squared (Born) rule from the wavefunction alone.
Allan F. Randall
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Axioms, 2023 This expository paper advocates an approach to physics in which “typicality” is identified with a suitable form of algorithmic randomness. To this end various theorems from mathematics and physics are reviewed.
Klaas Landsman
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Scholarpedia, 2007 Algorithmic "Solomonoff" Probability (AP) assigns to objects an a priori probability that is in some sense universal. This prior distribution has theoretical applications in a number of areas, including inductive inference theory and the time complexity analysis of algorithms.
Marcus Hutter +2 more
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