• Corpus ID: 17088853

Algorithmic randomness and stochastic selection function

@article{Takahashi2012AlgorithmicRA,
  title={Algorithmic randomness and stochastic selection function},
  author={Hayato Takahashi},
  journal={ArXiv},
  year={2012},
  volume={abs/1205.5504}
}

• Hayato Takahashi
• Published 24 May 2012
• Mathematics, Computer Science
• ArXiv

We show algorithmic randomness versions of the two classical theorems on subsequences of normal numbers. One is Kamae-Weiss theorem (Kamae 1973) on normal numbers, which characterize the selection function that preserves normal numbers. Another one is the Steinhaus (1922) theorem on normal numbers, which characterize the normality from their subsequences. In van Lambalgen (1987), an algorithmic analogy to Kamae-Weiss theorem is conjectured in terms of algorithmic randomness and complexity. In this paper we consider two types of algorithmic random sequence; one is ML-random sequences and the other one is the set of sequences that have maximal complexity rate. Then we show algorithmic randomness versions of corresponding theorems to the above classical results.