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Negation and redistribution with a preference — An information theoretic analysis

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  • Tanwar, Priya
  • Srivastava, Amit

Abstract

Yager (2014) defined the negation of a probability distribution using the idea that one can oppose the occurrence of any uncertain event by redistributing its probability equally among the other outcomes in a finite sample space. Equal redistribution signifies that probabilities are reallocated without preference to any particular outcome. The more the iterations of negation, the more uncertain the probability distribution becomes eventually converging to the maximum entropy state. However to apply the concept of negation in any uncertain environment, a detailed examination of the underlying structure of negation in probabilistic frameworks is required. In the present work, the definitions of negation of a discrete finite probability distribution proposed by various researchers has been reviewed and the way the concept of negation redefines probabilistic frameworks has been discussed. Also a generalized framework for redistribution of probabilities has been proposed. Finally the generalized framework has been compared with the original one(Yager’s Model).

Suggested Citation

  • Tanwar, Priya & Srivastava, Amit, 2023. "Negation and redistribution with a preference — An information theoretic analysis," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    • Handle: RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923004587
    DOI: 10.1016/j.chaos.2023.113557
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    References listed on IDEAS

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    1. Xiaozhuan Gao & Yong Deng, 2019. "The generalization negation of probability distribution and its application in target recognition based on sensor fusion," International Journal of Distributed Sensor Networks, , vol. 15(5), pages 15501477198, May.
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