Uncertainty analysis

For a much more detailed treatment, see Experimental uncertainty analysis

Uncertainty analysis investigates the uncertainty of variables that are used in decision-making problems in which observations and models represent the knowledge base. In other words, uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables.

In physical experiments uncertainty analysis, or experimental uncertainty assessment, deals with assessing the uncertainty in a measurement. An experiment designed to determine an effect, demonstrate a law, or estimate the numerical value of a physical variable will be affected by errors due to instrumentation, methodology, presence of confounding effects and so on. Experimental uncertainty estimates are needed to assess the confidence in the results.[1] A related field is design of experiments.

Likewise in numerical experiments and modelling uncertainty analysis draws upon a number of techniques for determining the reliability of model predictions, accounting for various sources of uncertainty in model input and design.[2] A related field is sensitivity analysis.

A calibrated parameter does not necessarily represent reality, as reality is much more complex. Any prediction has its own complexities of reality that cannot be represented uniquely in the calibrated model; therefore, there is a potential error. Such error must be accounted for when making management decisions on the basis of model outcomes. [3]

See also

References

  1. ↑ Summary of experimental uncertainty assessment methodology with example
  2. ↑ Methodologies for uncertainty analysis
  3. ↑ http://www.pesthomepage.org/Uncertainty_Analysis.php

Bibliography

This article is issued from Wikipedia - version of the Sunday, March 17, 2013. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.