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9780470740026

Simplicity, Complexity and Modelling

by ; ; ;
  • ISBN13:

    9780470740026

  • ISBN10:

    0470740027

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2011-11-07
  • Publisher: Wiley

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Summary

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 communication between 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 the model 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.General lessons for modelers are presented and the book is supported by downloadable datasets via website link.

Author Biography

Stephen Senn Professor of Statistics, Department of Statistics, University of Glasgow. Senior Professor and Editor of the Wiley Statistics in Practice Series. His research concerns statistics applied to drug development, a subject he has written many papers on.

Philip Dawid is a Professor in Statistics at Cambridge University. Researching fundamentally into the logical foundation of probability and statistics in terms of forecasting. He is currently an Editor of the journal Bayesian Analysis.

Mike Christie is Professor of Reservoir Engineering at Heriot-Way University. His research interests lie in accurate numerical modeling of fluid flow, in porous media primarily.

Andrew Cliffe Professor of Computational Applied Mathematics at the University of Nottingham. His research interests include fluid dynamics and nuclear waste disposal.

Table of Contents

Prefacep. ix
Acknowledgementsp. xi
Contributing authorsp. xiii
Introductionp. 1
The origins of the SCAM projectp. 1
The scope of modelling in the modern worldp. 2
The different professions and traditions engaged in modellingp. 3
Different types of modelsp. 3
Different purposes for modellingp. 5
The purpose of the bookp. 6
Overview of the chaptersp. 6
Referencesp. 8
Statistical model selectionp. 11
Introductionp. 11
Explanation or prediction?p. 12
Levels of uncertaintyp. 12
Bias-variance trade-offp. 13
Statistical modelsp. 15
Within-model inferencep. 16
Model comparisonp. 18
Bayesian model comparisonp. 18
Model uncertaintyp. 19
Laplace approximationp. 20
Penalized likelihoodp. 20
Bayesian information criterionp. 21
The Akaike information criterionp. 21
Inconsistency of AICp. 23
Significance testingp. 23
Many variablesp. 27
Data-driven approachesp. 28
Cross-validationp. 29
Prequential analysisp. 29
Model selection or model averaging?p. 30
Referencesp. 31
Modelling in drug developmentp. 35
Introductionp. 35
The nature of drug development and scope for statistical modellingp. 36
Simplicity versus complexity in phase III trialsp. 36
The nature of phase III trialsp. 36
The case for simplicity in analysing phase III trialsp. 37
The case for complexity in modelling clinical trialsp. 38
Some technical issuesp. 39
The effect of covariate adjustment in linear modelsp. 40
The effect of covariate adjustment in non-linear modelsp. 42
Random effects in multi-centre trialsp. 44
Subgroups and interactionsp. 45
Bayesian approachesp. 46
Conclusionp. 46
Appendix: The effect of covariate adjustment on the variance multiplier in least squaresp. 47
Referencesp. 48
Modelling with deterministic computer modelsp. 51
Introductionp. 51
Metamodels and emulators for computationally expensive simulatorsp. 52
Gaussian processes emulatorsp. 53
Multivariate outputsp. 56
Uncertainty analysisp. 57
Sensitivity analysisp. 58
Variance-based sensitivity analysisp. 58
Value of informationp. 61
Calibration and discrepancyp. 63
Discussionp. 64
Referencesp. 65
Modelling future climatesp. 69
Introductionp. 69
What is the risk from climate change?p. 70
Climate modelsp. 70
An anatomy of uncertaintyp. 72
Aleatoric uncertaintyp. 72
Epistemic uncertaintyp. 73
Simplicity and complexityp. 75
An example: The collapse of the thermohaline circulationp. 77
Conclusionsp. 79
Referencesp. 79
Modelling climate change impacts for adaptation assessmentsp. 83
Introductionp. 83
Climate impact assessmentp. 84
Modelling climate change impacts: From world development paths to localized impactsp. 87
Greenhouse gas emissionsp. 87
Climate modelsp. 90
Downscalingp. 93
Regional/local climate change impactsp. 94
Discussionp. 95
Multiple routes of uncertainty assessmentp. 96
What is the appropriate balance between simplicity and complexity?p. 96
Referencesp. 98
Modelling in water distribution systemsp. 103
Introductionp. 103
Water distribution system modelsp. 104
Water distribution systemsp. 104
WDS hydraulic modelsp. 104
Uncertainty in WDS hydraulic modellingp. 107
Calibration of WDS hydraulic modelsp. 108
Calibration problemp. 108
Existing approachesp. 109
Case studyp. 113
Sampling design for calibrationp. 116
Sampling design problemp. 116
Existing approachesp. 116
Case studyp. 120
Summary and conclusionsp. 120
Referencesp. 122
Modelling for flood risk managementp. 125
Introductionp. 125
Flood risk managementp. 126
Long-term changep. 130
Uncertaintyp. 131
Multi-purpose managementp. 131
Modelling for flood risk managementp. 132
Sourcep. 132
Pathwayp. 132
Receptorsp. 135
An example of a system model: Towynp. 135
Model choicep. 137
Conclusionsp. 143
Referencesp. 144
Uncertainty quantification and oil reservoir modellingp. 147
Introductionp. 147
Bayesian frameworkp. 148
Solution errorsp. 149
Quantifying uncertainty in prediction of oil recoveryp. 150
Stochastic sampling algorithmsp. 151
Computing uncertainties from multiple history matched modelsp. 153
Inverse problems and reservoir model history matchingp. 155
Synthetic problemsp. 155
Imperial college fault modelp. 157
Comparison of algorithms on a real field examplep. 158
Selecting appropriate detail in modelsp. 162
Adaptive multiscale estimationp. 162
Bayes factorsp. 165
Application of solution error modellingp. 167
Summaryp. 170
Referencesp. 171
Modelling in radioactive waste disposalp. 173
Introductionp. 173
The radioactive waste problemp. 174
What is radioactive waste?p. 174
How much radioactive waste is there?p. 175
What are the options for long-term management of radioactive waste?p. 175
The treatment of uncertainty in radioactive waste disposalp. 177
Deep geological disposalp. 177
Repository performance assessmentp. 177
Modellingp. 179
Model verification and validationp. 180
Strategies for dealing with uncertaintyp. 182
Summary and conclusionsp. 184
Referencesp. 184
Issues for modellersp. 187
What are models and what are they useful for?p. 187
Appropriate levels of complexityp. 189
Uncertaintyp. 190
Model inputs and parameter uncertaintyp. 190
Model uncertaintyp. 191
Referencesp. 192
Glossaryp. 193
Indexp. 201
Table of Contents provided by Ingram. All Rights Reserved.

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