This book is based upon an EPSRC funded project aimed at sharing understanding of modelling complex phenomena. The aim of the book is to ease communicationbetween modellers in different disciplines. The book is useful to physicists wishing to find out about statistical approaches to modelling and to statisticians wishing to learn about modelling in the physical sciences. It is also a useful source of modelling case histories. The book provides a much-needed reference guide for approaching statistical modeling and a useful source for modeling case histories. The book consists of four sections An introductory section covering important concepts in modeling and outlining different traditions as regards the relative utility of simple and complex modeling in statistics. Subject specific chapters illustrating modeling approaches in various disciplines A summary chapter explaining what issues have been resolved and which remain unresolved A glossary giving terms commonly used in different modeling traditions. This book covers a number of case studies of complex modeling, including climate change, flood risk, deterministic computer modeling and how well themodel can predict reality, water distribution systems and looks at how this has evolved, new drug development models and its usage and the petroleum industry and uncertainty of accurate forecasts. INDICE: Preface. Acknowledgements. 1 Introduction. 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. 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 Modelcomparison. 2.7 Bayesian model comparison. 2.7.1 Model uncertainty. 2.7.2 Laplace approximation. 2.8 Penalised likelihood. 2.8.1 Bayesian Information Criterion. 2.9 AIC. 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 analysis. 2.13 Model selection or model averaging? References. 3 Modelling in Drug Development. 3.1 Introduction. 3.2 The scope for statistical modelling in drug development. 3.2.1 The nature of drug development. 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 ComputerModels. 4.1 Introduction. 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 basedsensitivity analysis. 4.4.2 Value of information. 4.5 Calibration and discrepancy. 4.6 Discussion. References. 5 Modelling Future Climates. 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 for adaptation assessments. 6.1 Introduction. 6.1.1 Climate impact assessment. 6.2 Modelling climate change impacts: from world development paths to localised impacts. 6.2.1 Greenhouse gas emissions. 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 DistributionSystems. 7.1 Introduction. 7.2 Water Distribution System Models. 7.2.1 Water Distribution System. 7.2.2 WDS Hydraulic Mode
- ISBN: 978-0-470-74002-6
- Editorial: John Wiley & Sons
- Encuadernacion: Cartoné
- Páginas: 232
- Fecha Publicación: 28/10/2011
- Nº Volúmenes: 1
- Idioma: Inglés