|
:''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.〔( Summary of experimental uncertainty assessment methodology with example )〕 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.〔(Methodologies for uncertainty analysis )〕 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. 〔http://www.pesthomepage.org/Uncertainty_Analysis.php〕 ==See also== * Interval finite element method * Uncertainty quantification * Propagation of uncertainty * Measurement uncertainty#Uncertainty evaluation 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「uncertainty analysis」の詳細全文を読む スポンサード リンク
|