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Evidence theory (TE) uses maximum of entropy (ME) to quantify information, but its computation is complex. This study presents a modified algorithm that reduces computational steps, enhancing the applicability of ME in TE for incomplete information scenarios.

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Area of Science:

  • Information Theory
  • Decision Science
  • Mathematical Foundations

Background:

  • Classical probability theory (PT) struggles with inaccurate or incomplete information.
  • Evidence theory (TE) offers a framework for imprecise probabilities, with maximum of entropy (ME) quantifying evidence.
  • The computational complexity of ME has limited its practical application in TE.

Purpose of the Study:

  • To address the computational challenges associated with calculating the maximum of entropy (ME) in Evidence Theory (TE).
  • To propose a modified algorithm for ME calculation that improves efficiency.
  • To enhance the practical applicability of ME in TE for real-world problems involving uncertain data.

Main Methods:

  • A variation of the existing algorithm for calculating the maximum of entropy (ME) in Evidence Theory (TE) was developed.
  • The modified algorithm focuses on reducing the size of the power set of possibilities at each computational step.
  • The efficiency of the new algorithm was analyzed in terms of the number of computational steps required.

Main Results:

  • The modified algorithm significantly reduces the number of steps needed to compute the maximum of entropy (ME).
  • By reducing the power set size iteratively, the computational cost is lowered.
  • The proposed variation offers a more efficient approach to ME calculation within TE.

Conclusions:

  • The modified algorithm provides a more computationally tractable method for determining the maximum of entropy (ME) in Evidence Theory (TE).
  • This improvement is expected to increase the adoption and utility of ME for information quantification in TE.
  • The research facilitates broader application of TE in domains characterized by imprecise or incomplete data.