Algorithmic Information Theory

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Articles

  1. Search space topology, algorithmic information theory and biological evolution
  2. Algorithmic information theory | mathematics | sukarasri.cf
  3. A new version of algorithmic information theory

Finding community structure in networks using the eigenvectors of matrices.

Search space topology, algorithmic information theory and biological evolution

E 74 , Benczur, A. Approximating s-t minimum cuts in O n 2 -time. In Proc. Spielman, D. Graph sparsification by effective resistances. Spectral sparsification of graphs. SIAM J. Liu, M. Graphs determined by their signless Laplacian spectra. Linear Algebra 22 , — Granger, C. Investigating causal relations by econometric models and cross-spectral methods.

Econometrica 37 , — Schreiber, T. Measuring information transfer.


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Pearl, J. Solomonoff, R.

A formal theory of inductive inference: parts 1 and 2. Control 7 , 1—22—— Watanabe, S. Williams, P. Nonnegative decomposition of multivariate information.

Lizier, J. Information decomposition of target effects from multi-source interactions: perspectives on previous, current and future work. Entropy 20 , Li, M. The similarity metric. Theory 50 , — Bennett, C. Information distance. Theory 44 , — Cilibrasi, R. Clustering by compression.

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Algorithmic information theory | mathematics | sukarasri.cf

Numerical evaluation of the complexity of short strings: a glance into the innermost structure of algorithmic randomness. Soler-Toscano, F. Calculating Kolmogorov complexity from the frequency output distributions of small Turing machines. Hutter, M.

An algorithmic information theory of consciousness

Gauvrit, N. Rissanen, J. Modeling by shortest data description. Automatica 14 , — Levin, L. Universal search problems. Schmidhuber, J. The speed prior: a new simplicity measure yielding, near-optimal computable predictions. Daley, R. Minimal-program complexity of pseudo-recursive and pseudo-random sequences. Theory 9 , 83—94 Coding-theorem like behaviour and emergence of the universal distribution from resource-bounded algorithmic probability. Parallel Emergent Distrib. Computational measures of information gain and reinforcement in inference processes.

AI Commun. Universal and cognitive notions of part. The time scale of artificial intelligence: reflections on social effects. A decomposition method for global evaluation of Shannon entropy and local estimations of algorithmic complexity. Chaitin, G. On the length of programs for computing finite binary sequences. ACM 13 , — Laws of information conservation non-growth and aspects of the foundation of probability theory. Symmetry and correspondence of algorithmic complexity over geometric, spatial and topological representations.

Two-dimensional Kolmogorov complexity and validation of the coding theorem method by compressibility. PeerJ Comput. Riedel, J. Rule primality and compositional emergence of Turing-universality from elementary cellular automata. To build truly intelligent machines, teach them cause and effect. Quanta Magazine 15 May Minsky, M. The limits of understanding. Download references. All authors contributed to the writing of the paper. Correspondence to Hector Zenil or Narsis A.


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  • Algorithmic information theory?
  • Reprints and Permissions. Entropy Nucleic Acids Research The mathematical formalization of the concept of probability or chance has a long intertwined history. The now standard axioms of probability, learned by all students, are due to Kolmogorov While mathematically convincing, the semantics is far from clear.

    Frequentists interpret probabilities as limits of observed relative frequencies, objectivists think of them as real aspects of the world, subjectivists regard them as one's degree of belief often elicited from betting ratios , while Cournot only assigns meaning to events of high probability, namely as happening for sure in our world.


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    None of these approaches answers the question of whether some specific individual object or observation, like the binary strings above, is random. Kolmogorov's axioms do not allow one to ask such questions.

    A new version of algorithmic information theory

    Von Mises , with refinements to his approach by Wald , and Church with various degrees of success attempted to formalize the intuitive notion of one string looking more random than another see the example in the introduction. Unfortunately no sequence can satisfy all randomness tests. The Mises-Wald-Church approach seemed satisfactory until Ville showed that some sequences are random according to their definition and yet lack certain properties that are universally agreed to be satisfied by random sequences. Martin-Loef , rather than give a definition and check whether it satisfied all requirements, took the approach to formalize the notion of all effectively testable requirements in the form of tests for randomness.

    The tests are constructive namely all and only lower semi-computable ones, which are typically all one ever cares about. Since the tests are constructed from Turing machines, they can be effectively enumerated according to the effective enumeration of the Turing machines they derive from. Since the set of sequences satisfying a test having the randomness property the test verifies has measure one, and there are only countably many tests, the set of sequences satisfying all such tests also has measure one.