Single-experiment input uncertainty

Y. Lin, E. Song, B. L. Nelson*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


'Input uncertainty' refers to the simulation model risk caused by estimating input distributions from real-world data, and specifically the (usually unmeasured) variance in performance estimates that this introduces. We provide the first single-run method for quantifying input uncertainty, meaning that we derive our measure of input-uncertainty variance - both overall variance and the contribution to it of each input model - from the nominal experiment that the analyst would typically run using the estimated input models; other methods in the literature require additional diagnostic experiments. Application of our method is illustrated with two examples.

Original languageEnglish (US)
Pages (from-to)249-259
Number of pages11
JournalJournal of Simulation
Issue number3
StatePublished - Aug 16 2015


  • input modelling
  • model risk
  • output analysis

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation


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