PROBABILISTIC ALGORITHMIC RANDOMNESS.

We introduce martingales defined by probabilistic strategies, in which randomness is used to decide whether to bet. We show that different criteria for the success of computable probabilistic strategies can be used to characterize ML-randomness, computable randomness, and partial computable randomne...

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Publicado en:Journal of Symbolic Logic Vol. 78; no. 2; pp. 579 - 602
Autores principales: BUSS, SAM, MINNES, MIA
Formato: Artículo
Publicado: Cambridge University Press Jun2013
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: PROBABILISTIC ALGORITHMIC RANDOMNESS.
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          BUSS, SAM
          MINNES, MIA
        affil: DEPARTMENT OF MATHEMATICS, UNIVERSITY OF CALIFORNIA, SAN DIEGO, LA JOLLA, CA 92093-0112, USA
      su:
        Martingales (Mathematics)
        Probability theory
        Kolmogorov complexity
        Equivalence classes (Set theory)
        Mathematical functions
        Mathematical models
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        subj:
          Martingales (Mathematics)
          Probability theory
          Kolmogorov complexity
          Equivalence classes (Set theory)
          Mathematical functions
          Mathematical models
      ab: We introduce martingales defined by probabilistic strategies, in which randomness is used to decide whether to bet. We show that different criteria for the success of computable probabilistic strategies can be used to characterize ML-randomness, computable randomness, and partial computable randomness. Our characterization of ML-randomness partially addresses a critique of Schnorr by formulating ML randomness in terms of a computable process rather than a computably enumerable function.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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