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.

Sensitivity Analysis in Earth Observation Modeling highlights the state-of-the-art in ongoing research investigations and new applications of sensitivity analysis in earth observation modeling. In this framework, original works concerned with the development or exploitation of diverse methods applied to different types of earth observation data or earth observation-based modeling approaches are included. An overview of sensitivity analysis methods and principles is provided first, followed by examples of applications and case studies of different sensitivity/uncertainty analysis implementation methods, covering the full spectrum of sensitivity analysis techniques, including operational products. Finally, the book outlines challenges and future prospects for implementation in earth observation modeling. Information provided in this book is of practical value to readers looking to understand the principles of sensitivity analysis in earth observation modeling, the level of scientific maturity in the field, and where the main limitations or challenges are in terms of improving our ability to implement such approaches in a wide range of applications. Readers will also be informed on the implementation of sensitivity/uncertainty analysis on operational products available at present, on global and continental scales. All of this information is vital in the selection process of the most appropriate sensitivity analysis method to implement. - Outlines challenges and future prospects of sensitivity analysis implementation in earth observation modeling - Provides readers with a roadmap for directing future efforts - Includes case studies with applications from different regions around the globe, helping readers to explore strengths and weaknesses of the different methods in earth observation modeling - Presents a step-by-step guide, providing the principles of each method followed by the application of variants, making the reference easy to use and follow

This carefully edited collection synthesizes the state of the art in the theory and applications of designed experiments and their analyses. It provides a detailed overview of the tools required for the optimal design of experiments and their analyses. The handbook covers many recent advances in the field, including designs for nonlinear models and algorithms applicable to a wide variety of design problems. It also explores the extensive use of experimental designs in marketing, the pharmaceutical industry, engineering and other areas.

Summary Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Succeeding with AI sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It’s filled with practical techniques for running data science programs that ensure they’re cost effective and focused on the right business goals. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Succeeding with AI requires talent, tools, and money. So why do many well-funded, state-of-the-art projects fail to deliver meaningful business value? Because talent, tools, and money aren’t enough: You also need to know how to ask the right questions. In this unique book, AI consultant Veljko Krunic reveals a tested process to start AI projects right, so you’ll get the results you want. About the book Succeeding with AI sets out a framework for planning and running cost-effective, reliable AI projects that produce real business results. This practical guide reveals secrets forged during the author’s experience with dozens of startups, established businesses, and Fortune 500 giants that will help you establish meaningful, achievable goals. In it you’ll master a repeatable process to maximize the return on data-scientist hours and learn to implement effectiveness metrics for keeping projects on track and resistant to calcification. What's inside Where to invest for maximum payoff How AI projects are different from other software projects Catching early warnings in time to correct course Exercises and examples based on real-world business dilemmas About the reader For project and business leadership, result-focused data scientists, and engineering teams. No AI knowledge required. About the author Veljko Krunic is a data science consultant, has a computer science PhD, and is a certified Six Sigma Master Black Belt. Table of Contents: 1. Introduction 2. How to use AI in your business 3. Choosing your first AI project 4. Linking business and technology 5. What is an ML pipeline, and how does it affect an AI project? 6. Analyzing an ML pipeline 7. Guiding an AI project to success 8. AI trends that may affect you

This book presents foundations and practical application of multi-objective optimization methods to Vehicle Design Problems, bolstered with an extensive collection of examples. Opening with a broad theoretical introduction to the optimization of complex mechanical systems and multi-objective optimization methods, the book presents several applications which are extensively exposed here for the first time. The book includes examples of proposed methods to the solution of real vehicle design problems.

The aim of this book is to present qualitative aspects of logistics operations and supply chain management which help to implement the sustainable policy principles in the companies and public sector’s institutions. Authors in individual chapters address the issues related to reverse network configuration, forward and reverse supply chain integration, CO2 reduction in transportation, improvement of the production operations and management of the recovery activities. Some best practices from different countries and industries are presented. This book will be valuable to both academics and practitioners wishing to deepen their knowledge in the field of logistics operations and management with regard to sustainability issues.

Mathematical models are being used more and more widely to study complex dynamic systems (global weather, ecological systems, hydrological systems, nuclear reactors etc. including the specific subject of this book, crop-soil systems). The models are important aids in understanding, predicting and managing these systems. Such models are complex and imperfect. One fundamental research direction is to seek a better understanding of how these systems function, and to propose mathematical expressions embodying that understanding. However, this is not sufficient. It is also essential to have tools (often mathematical and statistical methods) to aid in developing, improving and using the models built from those equations. The book is specifically concerned with the application of methods to crop models, but much of the material is also applicable to dynamic system models in other fields. The goal of this book is to fill that gap.* State-of-the-art methods explained simply and illustrated specifically for crop models* Parameter estimation – applying statistical methods to the complex case of crop models, including Bayesian methods * Includes model evaluation, understanding and estimating prediction error* Offers a unique data assimilation by using the Kalman filter and beyond

This book presents the fundamental theories, methodologies and case studies of marine ecosystem modeling with a special focus on marine ecological dynamics that could provide scientists and researchers with a stabile and reliabile technical framework to study marine life and their developments.This book also clarifies the research objective and model classification methods of marine ecosystem dynamics research and analyzes the key marine ecological processes that affect modeling. The technical framework for improving the performance of modeling is also proposed, and the latest progress in research, as well as existing difficulties and challenges in end-to-end dynamics models are reviewed and analyzed. A dimensionality reduction theorem is established and derived for analyzing the stability of the solutions of a class of self-conserving marine ecosystem dynamic models. Also included in this work are several new types of marine ecosystem dynamics models constructed by modern computing methods — including artificial neural networks, cellular automata, and statistical dynamics — and case studies.This book is a suitable reference for professional and technical personnel, managers and graduate students specializing in the evolution mechanism, simulation, predication and regulation of marine ecosystems.

The success of Bioinformatics in recent years has been prompted by research in mole- lar biology and medicine in initiatives like the human genome project. The volume and diversification of data has increased so much that it is very hard if not impossible to analyze it by human experts. The analysis of this growing body of data, intensified by the development of a number of high-throughput experimental techniques that are generating the so called 'omics' data, has prompted for new computational methods. New global approaches, such as Systems Biology, have been emerging replacing the reductionist view that dominated biology research in the last decades, requiring the coordinated efforts of biological researchers with those related to data analysis, mathematical modelling and computer science. Computational methods have been helping in tasks related to knowledge discovery, modelling and optimization tasks. This workshop brings the opportunity to discuss applications of Bioinformatics and Computational Biology exploring the interactions between computer scientists, bio- gists and other scientific researchers. The IWPACBB technical program includes 29 papers (23 long papers and 6 short papers) selected from a submission pool of 51 papers, from 9 different countries. We thank the excellent work of the local organization members and also from the members of the Program Committee for their excellent reviewing work. October 2008 Juan M. Corchado Juan F. De Paz Miguel P. Rocha Florentino Fernández Riverola Organization

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures contains the plenary lectures and papers presented at the 11th International Conference on STRUCTURAL SAFETY AND RELIABILITY (ICOSSAR2013, New York, NY, USA, 16-20 June 2013), and covers major aspects of safety, reliability, risk and life-cycle performance of str