The Uncertainty Analysis of Model Results

propagation using simplerandom sampling (SRS)5.1.1 The meaning of confidence and confidence tolerance limits andintervals5.1.2 The mean value of the model result5.1.3 A quantile value of the model result5.1.4 A subjective probability interval for the model result5.1.5 Compliance of the model result with a limit value5.1.6 The sample variability of statistical tolerance limits5.1.7 Comparison of two model results5.1.8 Comparison of more than two model results5.2 Uncertainty statements available from uncertainty propagation using Latin5.2.1 Estimates of mean values of functions of the model result 5.2.2 The mean value of the model result5.2.3 A quantile value5.2.4 A subjective probability interval5.2.5 Compliance with a limit value5.2.6 Comparison of two model results5.2.7 Comparison of more than two model results5.2.8 Estimates from replicated Latin Hypercube samples5.3 Graphical presentation of uncertainty analysis results5.3.1 Graphical presentation of uncertainty analysis results from SRS5.3.2 Graphical presentation of uncertainty analysis results from LHS 5.4 References6 Step 5: Rank Uncertainties6.1 Introduction6.2 Differential sensitivity and "one-at-a-time" analysis6.3 Affordable measures for uncertainty importance ranking6.3.1 Uncertainty importance measures computed from raw data6.3.2 Uncertainty importance measures computed from rank transformed data6.3.3 Practical examples6.4 Explaining the outliers6.5 Contributions to quality assurance6.6 Graphical presentation of uncertainty importance measures6.7 Conclusions6.8 References7 Step 6: Present the Analysis and Interpret its Results7.1 Presentation of the analysis7.2 Interpretation of the uncertainty estimate7.3 Interpretation of the importance ranking8 Practical Execution of the Analysis8.1 Support by analysis software8.2 Comparison of four software packages8.3 References9 Uncertainty Analysis when Separation of Uncertainties is Required9.1 Introduction9.2 Step 1: Search9.3 Step 2: Quantify9.4 Step 3: Propagate9.4.1 Two nested Monte Carlo simulation loops9.4.2 Low probability extreme value answers9.5 Step 4: Estimate uncertainty9.6 Step 5: Rank uncertainties9.7 Step 6: Present the analysis and interpret its results9.8 References10 Practical Examples10.1 Introduction10.2 Uncertainty analysis of results from the application of a populationdynamics model10.2.1 The assessment questions10.2.2 The computer model10.2.3 The analysis tool10.2.4 The elicitation process10.2.5 The potentially important uncertainties10.2.6 Provisional state of knowledge quantifications10.2.7 State of knowledge dependences10.2.8 Model results obtained with best estimate parameter values10.2.9 Propagation of the state of knowledge through the model10.2.10 Uncertainty statements for selected model results10.2.11 Uncertainty importance statements for selected model results10.2.12 Conclusions10.3 Uncertainty analysis of results from the application of a dose reconstructionmodel10.3.1 The assessment questions10.3.2 The computer model10.3.3 The analysis tool10.3.4 The elicitation process10.3.5 The potentially important uncertainties10.3.6 The state of knowledge quantifications10.3.7 State of knowledge dependences10.3.8 Propagation of the state of knowledge through the model10.3.9 Why two Monte Carlo simulation loops?10.3.10 Answering the assessment questions10.3.11 Uncertainty importance statements for selected model results10.4 References