This book is a practical guide to the uncertainty analysis of computer model applications. Used in many areas, such as engineering, ecology and economics, computer models are subject to various uncertainties at the level of model formulations, parameter values and input data. Naturally, it would be advantageous to know the combined effect of these uncertainties on the model results as well as whether the state of knowledge should be improved in order to reduce the uncertainty of the results most effectively. The book supports decision-makers, model developers and users in their argumentation for an uncertainty analysis and assists them in the interpretation of the analysis results.
Introduction and Necessary Distinctions
1.1 The application of computer models
1.2 Sources of epistemic uncertainty
1.3 Verification and validation
1.4 Why perform an analysis of epistemic uncertainty
1.5 Source of aleatoric uncertainty
1.6 Two different interpretations of probability
1.7 Separation of uncertainties
1.8 References
2 Step 1: Search
2.1 The scenario description
2.2 The conceptual model
2.3 The mathematical model
2.4 The numerical model
2.5 Conclusion
3 Step 2: Quantify
3.1 Subjective probability
3.2 Data versus model uncertainty
3.3 Ways to quantify data uncertainty
3.3.1 Measurable quantities as uncertain data
3.3.2 Functions of measurable quantities
3.3.3 Distributions fitted to measurable quantities
3.3.4 Sequences of uncertain input data
3.3.5 Special cases
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