Introduction to imprecise probabilities

Daniel Krpelík*, Tathagata Basu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Since uncertainty is persistent in engineering analyses, this chapter aimed to introduce methods to describe and reason with under uncertainty in various scenarios. Probability theory is the most widely used methodology for uncertainty quantification for a long time and has proven to be a powerful tool for this task. Nevertheless, the construction of stochastic models relies on very fine information, such as large amount of observations, which is not always available. Without it, the constructed models are only very rough approximations of the real laws and may cause incorrect decisions. In this chapter, we introduce other types of models, based on the theory of imprecise probability, which we are able to construct and reason with under situations with limited available knowledge.

Original languageEnglish
Title of host publicationOptimization Under Uncertainty with Applications to Aerospace Engineering
Place of PublicationCham, Switzerland
PublisherSpringer International Publishing AG
Pages35-79
Number of pages45
ISBN (Electronic)9783030601669
ISBN (Print)9783030601652
DOIs
Publication statusPublished - 15 Feb 2021
Externally publishedYes

Keywords

  • imprecise probability
  • lower previsions
  • robust inference
  • uncertainty

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