Reliable Computing

, Volume 13, Issue 1, pp 25–69 | Cite as

Monte-Carlo-Type Techniques for Processing Interval Uncertainty, and Their Potential Engineering Applications

  • Vladik Kreinovich
  • Jan Beck
  • Carlos Ferregut
  • Araceli Sanchez
  • G. Randy Keller
  • Matthew Averill
  • Scott A. Starks


In engineering applications, we need to make decisions under uncertainty. Traditionally, in engineering, statistical methods are used, methods assuming that we know the probability distribution of different uncertain parameters. Usually, we can safely linearize the dependence of the desired quantities y (e.g., stress at different structural points) on the uncertain parameters x i–thus enabling sensitivity analysis. Often, the number n of uncertain parameters is huge, so sensitivity analysis leads to a lot of computation time. To speed up the processing, we propose to use special Monte-Carlo-type simulations.


Uncertain Parameter Epistemic Uncertainty Interval Uncertainty Cauchy Distribution Conditional Average 
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Copyright information

© Springer Science + Business Media B.V. 2007

Authors and Affiliations

  • Vladik Kreinovich
    • 1
  • Jan Beck
    • 1
  • Carlos Ferregut
    • 1
  • Araceli Sanchez
    • 1
  • G. Randy Keller
    • 1
  • Matthew Averill
    • 1
  • Scott A. Starks
    • 1
  1. 1.College of Engineering and NASA Pan-American Center for Earth and Environmental Studies (PACES)University of TexasEl PasoUSA

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