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The Tsallis entropy of natural information

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  • Sneddon, Robert

Abstract

Estimating the information contained in natural data, such as electroencephalography data, is unusually difficult because the relationship between the physical data and the information that it encodes is unknown. This unknown relationship is often called the encoding problem. The present work provides a solution to this problem by deriving a method to estimate the Tsallis entropy in natural data. The method is based on two findings. The first finding is that the physical instantiation of any information event, that is, the physical occurrence of a symbol of information, must begin and end at a discontinuity or critical point (maximum, minimum, or saddle point) in the data. The second finding is that, in certain data types such as the encephalogram (EEG), the variance within of an EEG waveform event is directly proportional to its probability of occurrence.

Suggested Citation

  • Sneddon, Robert, 2007. "The Tsallis entropy of natural information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 101-118.
    • Handle: RePEc:eee:phsmap:v:386:y:2007:i:1:p:101-118
    DOI: 10.1016/j.physa.2007.05.065
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    Cited by:

    1. Papapetrou, M. & Kugiumtzis, D., 2020. "Tsallis conditional mutual information in investigating long range correlation in symbol sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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