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Complex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard. Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative sensitivity analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications. The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials. Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications. Is authored by leading sensitivity analysis practitioners, combining a range of disciplinary backgrounds. Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk analysis and to financial analysts concerned with pricing and hedging.
This Research Agenda explores the future of spatial analysis, and how the field informs and challenges the policy landscape. A wide range of contributors from different intellectual communities address the problem of causality in geographic analysis, arguing that diversity is crucial for the future success of the discipline. This title contains one or more Open Access chapters.
The book presents the select proceedings of the 8th International Conference on Transportation Systems Engineering and Management (CTSEM 2021). The book covers topics pertaining to three broad areas of transportation engineering, namely Transportation Planning, Traffic Engineering and Pavement Technology. The topics covered include transportation and land use, urban and regional transportation planning, travel behavior modeling, travel demand analysis, forecasting and management, transportation and ICT, public transport planning and management, freight transport, traffic flow modeling and management, highway design and maintenance, capacity and level of service, traffic crashes and safety, ITS and applications, non-motorized transportation, transportation economics and policy, road and parking pricing, pedestrian facilities and safety, road asset management, pavement materials and characterization, pavement design and construction, pavement evaluation and management, transportation infrastructure financing, innovative trends in transportation systems, sustainable transportation, smart cities, resilience of transportation systems and environmental and ecological aspects. This book will be useful for the students, researchers and the professionals in the area of civil engineering, especially transportation and traffic engineering.