• DocumentCode
    1120435
  • Title

    Being Sensitive to Uncertainty

  • Author

    Arriola, Leon M. ; Hyman, James M.

  • Author_Institution
    Univ. of Wisconsin-Whitewater, Wisconsin, WI
  • Volume
    9
  • Issue
    2
  • fYear
    2007
  • Firstpage
    10
  • Lastpage
    20
  • Abstract
    Predictive modeling´s effectiveness is hindered by inherent uncertainties in the input parameters. Sensitivity and uncertainty analysis quantify these uncertainties and identify the relationships between input and output variations, leading to the construction of a more accurate model. This survey introduces the application, implementation, and underlying principles of sensitivity and uncertainty quantification
  • Keywords
    sensitivity analysis; stochastic processes; predictive modeling; sensitivity analysis; uncertainty analysis; Algorithm design and analysis; Computational modeling; Diseases; Input variables; Measurement uncertainty; Predictive models; Probability density function; Sampling methods; Sensitivity analysis; Statistical analysis; analysis; sensitivity; stochastic; uncertainty; volatility;
  • fLanguage
    English
  • Journal_Title
    Computing in Science & Engineering
  • Publisher
    ieee
  • ISSN
    1521-9615
  • Type

    jour

  • DOI
    10.1109/MCSE.2007.27
  • Filename
    4100925
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1120435