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Analyzing Sample Size in Information-Theoretic Models

Author

Listed:
  • D. Bernal-Casas

    (Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain)

  • J. M. Oller

    (Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, 08028 Barcelona, Spain)

Abstract

In this paper, we delve into the complexities of information-theoretic models, specifically focusing on how we can model sample size and how it affects our previous findings. This question is fundamental and intricate, posing a significant intellectual challenge to our research. While previous studies have considered a fixed sample size, this work explores other possible alternatives to assess its impact on the mathematical approach. To ensure that our framework aligns with the principles of quantum theory, specific conditions related to sample size must be met, as they are inherently linked to information quantities. The arbitrary nature of sample size presents a significant challenge in achieving this alignment, which we thoroughly investigate in this study.

Suggested Citation

  • D. Bernal-Casas & J. M. Oller, 2024. "Analyzing Sample Size in Information-Theoretic Models," Mathematics, MDPI, vol. 12(24), pages 1-15, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:24:p:4018-:d:1549333
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    References listed on IDEAS

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    2. Seth Lloyd, 2000. "Ultimate physical limits to computation," Nature, Nature, vol. 406(6799), pages 1047-1054, August.
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