Towards a Better Understandability of Uncertainty-Estimating Algorithms

  • Andrew PownukEmail author
  • Vladik Kreinovich
Part of the Studies in Computational Intelligence book series (SCI, volume 773)


In this chapter, we explain how to make uncertainty-estimating algorithms easier to understand. We start with the case of interval uncertainty. For this case, in Sect. 4.1, we provide an intuitive explanation for different types of solutions, and in Sect. 4.2, we provide an intuitive explanation for seemingly counter-intuitive methods for solving the corresponding problem. In Sect. 4.3, we consider the case of probabilistic uncertainty. For this case, we explain why it is reasonable to consider mixtures of probability distributions. In Sect. 4.4, we consider the case of fuzzy uncertainty; we explain why seemingly non-traditional fuzzy ideas are, in effect, very similar to the ideas from traditional logic.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computational Science ProgramUniversity of Texas at El PasoEl PasoUSA

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