A Generalized Characterization of Algorithmic Probability

Tom F. Sterkenburg*

*Bijbehorende auteur voor dit werk

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    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 the class of transformations, by all compatible universal monotone Turing machines, of any continuous computable measure in place of the uniform measure. Some consideration is given to possible implications for the association of algorithmic probability with certain foundational principles of statistics.

    Originele taal-2English
    Pagina's (van-tot)1337-1352
    Aantal pagina's16
    TijdschriftTheory of computing systems
    Volume61
    Nummer van het tijdschrift4
    DOI's
    StatusPublished - nov-2017
    Evenement10th International Conference on Computability, Complexity and Randomness (CCR) - Heidelberg, Germany
    Duur: 22-jun-201526-jun-2015

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