Beyond p-Boxes and Interval-Valued Moments: Natural Next Approximations to General Imprecise Probabilities

  • Olga Kosheleva
  • Vladik KreinovichEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 892)


To make an adequate decision, we need to know the probabilities of different consequences of different actions. In practice, we only have partial information about these probabilities—this situation is known as imprecise probabilities. A general description of all possible imprecise probabilities requires using infinitely many parameters. In practice, the two most widely used few-parametric approximate descriptions are p-boxes (bounds on the values of the cumulative distribution function) and interval-valued moments (i.e., bounds on moments). In some situations, these approximations are not sufficiently accurate. So, we need more accurate more-parametric approximations. In this paper, we explain what are the natural next approximations.



This work was supported in part by the National Science Foundation grants 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science) and HRD-1242122 (Cyber-ShARE Center of Excellence).


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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.University of Texas at El PasoEl PasoUSA

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