Abstract
'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 language | English (US) |
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Pages (from-to) | 249-259 |
Number of pages | 11 |
Journal | Journal of Simulation |
Volume | 9 |
Issue number | 3 |
DOIs | |
State | Published - Aug 16 2015 |
All Science Journal Classification (ASJC) codes
- Software
- Modeling and Simulation
- Management Science and Operations Research
- Industrial and Manufacturing Engineering