| Description |
xiv, 205 pages : illustrations (chiefly color), map ; 24 cm |
| Series |
Statistics in practice |
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Statistics in practice.
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| Contents |
Contents note continued: 10.2.The radioactive waste problem -- 10.2.1.What is radioactive waste? -- 10.2.2.How much radioactive waste is there? -- 10.2.3.What are the options for long-term management of radioactive waste? -- 10.3.The treatment of uncertainty in radioactive waste disposal -- 10.3.1.Deep geological disposal -- 10.3.2.Repository performance assessment -- 10.3.3.Modelling -- 10.3.4.Model verification and validation -- 10.3.5.Strategies for dealing with uncertainty -- 10.4.Summary and conclusions -- References -- 11.Issues for modellers / Stephen Senn -- 11.1.What are models and what are they useful for? -- 11.2.Appropriate levels of complexity -- 11.3.Uncertainty -- 11.3.1.Model inputs and parameter uncertainty -- 11.3.2.Model uncertainty -- References |
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Contents note continued: 2.13.Model selection or model averaging? -- References -- 3.Modelling in drug development / Stephen Senn -- 3.1.Introduction -- 3.2.The nature of drug development and scope for statistical modelling -- 3.3.Simplicity versus complexity in phase III trials -- 3.3.1.The nature of phase III trials -- 3.3.2.The case for simplicity in analysing phase III trials -- 3.3.3.The case for complexity in modelling clinical trials -- 3.4.Some technical issues -- 3.4.1.The effect of covariate adjustment in linear models -- 3.4.2.The effect of covariate adjustment in non-linear models -- 3.4.3.Random effects in multi-centre trials -- 3.4.4.Subgroups and interactions -- 3.4.5.Bayesian approaches -- 3.5.Conclusion -- 3.6.Appendix: The effect of covariate adjustment on the variance multiplier in least squares -- References -- 4.Modelling with deterministic computer models / Jeremy E. Oakley -- 4.1.Introduction -- |
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Contents note continued: 4.2.Metamodels and emulators for computationally expensive simulators -- 4.2.1.Gaussian processes emulators -- 4.2.2.Multivariate outputs -- 4.3.Uncertainty analysis -- 4.4.Sensitivity analysis -- 4.4.1.Variance-based sensitivity analysis -- 4.4.2.Value of information -- 4.5.Calibration and discrepancy -- 4.6.Discussion -- References -- 5.Modelling future climates / Robin Tokmakian -- 5.1.Introduction -- 5.2.What is the risk from climate change? -- 5.3.Climate models -- 5.4.An anatomy of uncertainty -- 5.4.1.Aleatoric uncertainty -- 5.4.2.Epistemic uncertainty -- 5.5.Simplicity and complexity -- 5.6.An example: The collapse of the thermohaline circulation -- 5.7.Conclusions -- References -- 6.Modelling climate change impacts Tor adaptation assessments / Jeroen van der Sluijs -- 6.1.Introduction -- 6.1.1.Climate impact assessment -- 6.2.Modelling climate change impacts: From world development paths to localized impacts -- 6.2.1.Greenhouse gas emissions -- |
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Contents note continued: 6.2.2.Climate models -- 6.2.3.Downscaling -- 6.2.4.Regional/local climate change impacts -- 6.3.Discussion -- 6.3.1.Multiple routes of uncertainty assessment -- 6.3.2.What is the appropriate balance between simplicity and complexity? -- References -- 7.Modelling in water distribution systems / Zoran Kapelan -- 7.1.Introduction -- 7.2.Water distribution system models -- 7.2.1.Water distribution systems -- 7.2.2.WDS hydraulic models -- 7.2.3.Uncertainty in WDS hydraulic modelling -- 7.3.Calibration of WDS hydraulic models -- 7.3.1.Calibration problem -- 7.3.2.Existing approaches -- 7.3.3.Case study -- 7.4.Sampling design for calibration -- 7.4.1.Sampling design problem -- 7.4.2.Existing approaches -- 7.4.3.Case study -- 7.5.Summary and conclusions -- References -- 8.Modelling for flood risk management / Jim Hall -- 8.1.Introduction -- 8.2.Flood risk management -- 8.2.1.Long-term change -- 8.2.2.Uncertainty -- 8.3.Multi-purpose management -- |
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Contents note continued: 8.4.Modelling for flood risk management -- 8.4.1.Source -- 8.4.2.Pathway -- 8.4.3.Receptors -- 8.4.4.An example of a system model: Towyn -- 8.5.Model choice -- 8.6.Conclusions -- References -- 9.Uncertainty quantification and oil reservoir modelling / Mike Christie -- 9.1.Introduction -- 9.2.Bayesian framework -- 9.2.1.Solution errors -- 9.3.Quantifying uncertainty in prediction of oil recovery -- 9.3.1.Stochastic sampling algorithms -- 9.3.2.Computing uncertainties from multiple history matched models -- 9.4.Inverse problems and reservoir model history matching -- 9.4.1.Synthetic problems -- 9.4.2.Imperial college fault model -- 9.4.3.Comparison of algorithms on a real field example -- 9.5.Selecting appropriate detail in models -- 9.5.1.Adaptive multiscale estimation -- 9.5.2.Bayes factors -- 9.5.3.Application of solution error modelling -- 9.6.Summary -- References -- 10.Modelling in radioactive waste disposal / Andrew Cliffe -- 10.1.Introduction -- |
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Machine generated contents note: 1.Introduction / Stephen Senn -- 1.1.The origins of the SCAM project -- 1.2.The scope of modelling in the modern world -- 1.3.The different professions and traditions engaged in modelling -- 1.4.Different types of models -- 1.5.Different purposes for modelling -- 1.6.The purpose of the book -- 1.7.Overview of the chapters -- References -- 2.Statistical model selection / Stephen Senn -- 2.1.Introduction -- 2.2.Explanation or prediction? -- 2.3.Levels of uncertainty -- 2.4.Bias-variance trade-off -- 2.5.Statistical models -- 2.5.1.Within-model inference -- 2.6.Model comparison -- 2.7.Bayesian model comparison -- 2.7.1.Model uncertainty -- 2.7.2.Laplace approximation -- 2.8.Penalized likelihood -- 2.8.1.Bayesian information criterion -- 2.9.The Akaike information criterion -- 2.9.1.Inconsistency of AIC -- 2.10.Significance testing -- 2.11.Many variables -- 2.12.Data-driven approaches -- 2.12.1.Cross-validation -- 2.12.2.Prequential analysis -- |
| Notes |
Formerly CIP. Uk |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Computational complexity.
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Mathematical models.
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Simulation methods.
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| Author |
Senn, Stephen.
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| LC no. |
2011020649 |
| ISBN |
0470740027 (hbk.) |
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9780470740026 (hbk.) |
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(oBook) |
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(ePDF) |
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(ePub) |
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(Mobi) |
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