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Dorota Kurowicka Uncertainty Analysis with High Dimensional Dependence Modelling


Mathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty. Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. In recent years there has been an explosion of interest in uncertainty analysis. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are among the active research areas. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: An up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis. All the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion. Numerous worked examples and applications. Workbook problems, enabling use for teaching. Software support for the examples, using UNICORN – a Windows-based uncertainty modelling package developed by the authors. A website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples. Uncertainty Analysis with High Dimensional Dependence Modelling offers a comprehensive exploration of a new emerging field. It will prove an invaluable text for researches, practitioners and graduate students in areas ranging from statistics and engineering to reliability and environmetrics.

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W. Steele Glenn Experimentation, Validation, and Uncertainty Analysis for Engineers


Helps engineers and scientists assess and manage uncertainty at all stages of experimentation and validation of simulations Fully updated from its previous edition, Experimentation, Validation, and Uncertainty Analysis for Engineers, Fourth Edition includes expanded coverage and new examples of applying the Monte Carlo Method (MCM) in performing uncertainty analyses. Presenting the current, internationally accepted methodology from ISO, ANSI, and ASME standards for propagating uncertainties using both the MCM and the Taylor Series Method (TSM), it provides a logical approach to experimentation and validation through the application of uncertainty analysis in the planning, design, construction, debugging, execution, data analysis, and reporting phases of experimental and validation programs. It also illustrates how to use a spreadsheet approach to apply the MCM and the TSM, based on the authors’ experience in applying uncertainty analysis in complex, large-scale testing of real engineering systems. Experimentation, Validation, and Uncertainty Analysis for Engineers, Fourth Edition includes examples throughout, contains end of chapter problems, and is accompanied by the authors’ website www.uncertainty-analysis.com. Guides readers through all aspects of experimentation, validation, and uncertainty analysis Emphasizes the use of the Monte Carlo Method in performing uncertainty analysis Includes complete new examples throughout Features workable problems at the end of chapters Experimentation, Validation, and Uncertainty Analysis for Engineers, Fourth Edition is an ideal text and guide for researchers, engineers, and graduate and senior undergraduate students in engineering and science disciplines. Knowledge of the material in this Fourth Edition is a must for those involved in executing or managing experimental programs or validating models and simulations.

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Terje Aven Foundations of Risk Analysis


Foundations of Risk Analysis presents the issues core to risk analysis – understanding what risk means, expressing risk, building risk models, addressing uncertainty, and applying probability models to real problems. The author provides the readers with the knowledge and basic thinking they require to successfully manage risk and uncertainty to support decision making. This updated edition reflects recent developments on risk and uncertainty concepts, representations and treatment. New material in Foundations of Risk Analysis includes: An up to date presentation of how to understand, define and describe risk based on research carried out in recent years. A new definition of the concept of vulnerability consistent with the understanding of risk. Reflections on the need for seeing beyond probabilities to measure/describe uncertainties. A presentation and discussion of a method for assessing the importance of assumptions (uncertainty factors) in the background knowledge that the subjective probabilities are based on A brief introduction to approaches that produce interval (imprecise) probabilities instead of exact probabilities. In addition the new version provides a number of other improvements, for example, concerning the use of cost-benefit analyses and the As Low As Reasonably Practicable (ALARP) principle. Foundations of Risk Analysis provides a framework for understanding, conducting and using risk analysis suitable for advanced undergraduates, graduates, analysts and researchers from statistics, engineering, finance, medicine and the physical sciences, as well as for managers facing decision making problems involving risk and uncertainty.

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Philippe Weber Data Uncertainty and Important Measures


The first part of the book defines the concept of uncertainties and the mathematical frameworks that will be used for uncertainty modeling. The application to system reliability assessment illustrates the concept. In the second part, evidential networks as a new tool to model uncertainty in reliability and risk analysis is proposed and described. Then it is applied on SIS performance assessment and in risk analysis of a heat sink. In the third part, Bayesian and evidential networks are used to deal with important measures evaluation in the context of uncertainties.

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Peter Atkinson M. Uncertainty in Remote Sensing and GIS


Remote sensing and geographical information science (GIS) have advanced considerably in recent years. However, the potential of remote sensing and GIS within the environmental sciences is limited by uncertainty, especially in connection with the data sets and methods used. In many studies, the issue of uncertainty has been incompletely addressed. The situation has arisen in part from a lack of appreciation of uncertainty and the problems it can cause as well as of the techniques that may be used to accommodate it. This book provides general overviews on uncertainty in remote sensing and GIS that illustrate the range of uncertainties that may occur, in addition to describing the means of measuring uncertainty and the impacts of uncertainty on analyses and interpretations made. Uncertainty in Remote Sensing and GIS provides readers with comprehensive coverage of this largely undocumented subject: * Relevant to a broad variety of disciplines including geography, environmental science, electrical engineering and statistics * Covers range of material from base overviews to specific applications * Focuses on issues connected with uncertainty at various points along typical data analysis chains used in remote sensing and GIS Written by an international team of researchers drawn from a variety of disciplines, Uncertainty in Remote Sensing and GIS provides focussed discussions on topics of considerable importance to a broad research and user community. The book is invaluable reading for researchers, advanced students and practitioners who want to understand the nature of uncertainty in remote sensing and GIS, its limitations and methods of accommodating it.

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Robert W. Fitzgerald Fire Performance Analysis for Buildings


A building fire is dynamic. A continually changing hostile fire environment influences time relationships that affect fire defenses and risks to people and building functions. The fire and fire defenses in each building interact with different sequences and distinct ways. Risks are characterized by the building’s performance. Significantly updated and restructured new edition Fire Performance Analysis for Buildings, 2nd Edition organizes the complex interactions into an analytical framework to evaluate any building – at any location – built under any regulatory jurisdiction or era. Systematic, logical procedures evaluate individual component behavior and integrate results to understand holistic performance. The Interactive Performance Information (IPI) chart structures complex time-related interactions among the fire, fire defenses, and associated risks. Quantification uses state-of-the-art deterministic methods of fire safety engineering and fire science. Managing uncertainty is specifically addressed. Key features: Emphasizes fire performance analysis for new or existing buildings. Augments fire dynamics calculation methods with qualitative methods to form a more complete understanding of the effects of hostile fire characteristics on building performance. Describes fire ground operations for engineers with no fire service experience. An analysis evaluates ways the site and building design help or hinder manual fire suppression. Establishes a transition from traditional structural requirements to modern calculation based structural analysis and design for fire conditions. Structural concepts are described for non-structural engineers to enable the roles of each profession to be integrated into comprehensive performance evaluations. Addresses techniques of managing uncertainty to improve understanding and communication with professionals of other disciplines. Describes methods of risk management using information from the building’s performance analysis. Fire Performance Analysis for Buildings, 2nd Edition has been completely restructured around a performance based framework. Applications integrate traditional fire defenses with fire science and engineering to combine component performance with holistic performance.

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Группа авторов Natural Hazard Uncertainty Assessment


Uncertainties are pervasive in natural hazards, and it is crucial to develop robust and meaningful approaches to characterize and communicate uncertainties to inform modeling efforts. In this monograph we provide a broad, cross-disciplinary overview of issues relating to uncertainties faced in natural hazard and risk assessment. We introduce some basic tenets of uncertainty analysis, discuss issues related to communication and decision support, and offer numerous examples of analyses and modeling approaches that vary by context and scope. Contributors include scientists from across the full breath of the natural hazard scientific community, from those in real-time analysis of natural hazards to those in the research community from academia and government. Key themes and highlights include: Substantial breadth and depth of analysis in terms of the types of natural hazards addressed, the disciplinary perspectives represented, and the number of studies included Targeted, application-centered analyses with a focus on development and use of modeling techniques to address various sources of uncertainty Emphasis on the impacts of climate change on natural hazard processes and outcomes Recommendations for cross-disciplinary and science transfer across natural hazard sciences This volume will be an excellent resource for those interested in the current work on uncertainty classification/quantification and will document common and emergent research themes to allow all to learn from each other and build a more connected but still diverse and ever growing community of scientists.

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George Klir J. Uncertainty and Information


Deal with information and uncertainty properly and efficiently using tools emerging from generalized information theory Uncertainty and Information: Foundations of Generalized Information Theory contains comprehensive and up-to-date coverage of results that have emerged from a research program begun by the author in the early 1990s under the name «generalized information theory» (GIT). This ongoing research program aims to develop a formal mathematical treatment of the interrelated concepts of uncertainty and information in all their varieties. In GIT, as in classical information theory, uncertainty (predictive, retrodictive, diagnostic, prescriptive, and the like) is viewed as a manifestation of information deficiency, while information is viewed as anything capable of reducing the uncertainty. A broad conceptual framework for GIT is obtained by expanding the formalized language of classical set theory to include more expressive formalized languages based on fuzzy sets of various types, and by expanding classical theory of additive measures to include more expressive non-additive measures of various types. This landmark book examines each of several theories for dealing with particular types of uncertainty at the following four levels: * Mathematical formalization of the conceived type of uncertainty * Calculus for manipulating this particular type of uncertainty * Justifiable ways of measuring the amount of uncertainty in any situation formalizable in the theory * Methodological aspects of the theory With extensive use of examples and illustrations to clarify complex material and demonstrate practical applications, generous historical and bibliographical notes, end-of-chapter exercises to test readers' newfound knowledge, glossaries, and an Instructor's Manual, this is an excellent graduate-level textbook, as well as an outstanding reference for researchers and practitioners who deal with the various problems involving uncertainty and information. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

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Uncertainty


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Dorota KUROWICKA | Professor (Associate) | Delft ...

Dorota Kurowicka Roger Cooke This paper extends the results in [D. Kurowicka, R.M. Cooke, A parametrization of positive definite matrices in terms of partial correlation vines, Linear Algebra Appl ...

Dorota Kurowicka - Associate Professor - TU Delft | LinkedIn

Dorota Kurowicka’s main research interests lie in areas of Risk and Uncertainty Analysis, in particular in dependence modeling for uncertainty analysis. The most important contribution of her...

‪Dorota Kurowicka‬ - ‪Google Scholar‬

‪Delft University of Technology‬ - ‪Cited by 5,016‬ - ‪dependece‬ - ‪graphical models‬ - ‪risk analysis‬

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Bekijk het profiel van Dorota Kurowicka op LinkedIn, de grootste professionele community ter wereld. Dorota heeft 1 functie op zijn of haar profiel. Bekijk het volledige profiel op LinkedIn om de connecties van Dorota en vacatures bij vergelijkbare bedrijven te zien.

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Dorota Kurowicka, Roger M. Cooke: Uncertainty Analysis ...

Dorota Kurowicka, Roger M. Cooke: Uncertainty Analysis with High Dimensional Dependence Modelling - Sprache: Englisch. Dateigröße in MByte: 10. (eBook pdf) - bei eBook.de

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Dorota Kurowicka – Wetenschap en Techniek

Dorota Kurowicka werd geboren in Olstyn en bracht haar jeugd door in Warmië. Na de middelbare school ging ze wiskunde studeren aan de Universiteit van Gdańsk. Haar studie in Gdańsk was erg motiverend. Haar studentenflat stond 200 meter verwijderd van het huis van Lech Wałęsa.

Dorota Kurowicka – Nauka i Technika

Dorota Kurowicka urodziła się w Olsztynie i spędziła swoje dzieciństwo na Warmii. Po szkole średniej podjęła studia na Universytecie w Gdańsku. Zdecydowała się studiować matematykę. Studia w Gdańsku były bardzo stymulujące.

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dblp: Dorota Kurowicka

List of computer science publications by Dorota Kurowicka In view of the current Corona Virus epidemic, Schloss Dagstuhl has moved its 2020 proposal submission period to July 1 to July 15, 2020 , and there will not be another proposal round in November 2020.

Uncertainty and Sensitivity Analyses of a Dynamic Economic ...

With public health policy increasingly relying on mathematical models to provide insights about the impacts of potential policy options, the demand for uncertainty and sensitivity analyses that exp...

Dorota Kurowicka, Roger M. Cooke: Uncertainty Analysis ...

Dorota Kurowicka, Roger M. Cooke: Uncertainty Analysis with High Dimensional Dependence Modelling - Sprache: Englisch. (Buch (gebunden)) - bei eBook.de

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Dorota Kurowicka: 2013

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Uncertainty and Sensitivity Analyses of a Dynamic Economic ...

Dorota Kurowicka, PhD. Delft University of Technology, Department of Mathematics, Delft, the Netherlands. Roger M. Cooke, PhD. Delft University of Technology, Department of Mathematics, Delft, the Netherlands. PMID: 18349438 Abstract References Abstract. With public health policy increasingly relying on mathematical models to provide insights about the impacts of potential policy options, the ...

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Semantic Scholar profile for Dorota Kurowicka, with 192 highly influential citations and 76 scientific research papers.

Kurowicka, D: Uncertainty Analysis with High Dimensional ...

Kurowicka, D: Uncertainty Analysis with High Dimensional Dep (Wiley Series in Probability and Statistics) | Kurowicka, Dorota, Cooke, Roger M. | ISBN: 9780470863060 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

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Dependence Modeling: Vine Copula Handbook | Dorota ...

Dorota Kurowicka, Harry Joe, Editors. This book is a collaborative effort from three workshops held over the last three years, all involving principal contributors to the vine-copula methodology. Research and applications in vines have been growing rapidly and there is now a growing need to collate basic results, and standardize terminology and methods. Specifically, this handbook will (1 ...

Uncertainty Analysis with High Dimensional Dependence ...

Uncertainty Analysis with High Dimensional Dependence Modelling - ISBN: 9780470863084 - (ebook) - von Dorota Kurowicka, Roger M. Cooke, Verlag: Wiley

Dependence Modeling: Vine Copula Handbook by Dorota ...

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Dependence Modeling - World Scientific

Though dating back to 1959 when the term “copulae” was coined, copula models only started their triumphal procession in the mid-1990s. Application of copulae was primarily restricted to the world of finance and insurance but now the copula concept has found its way into nearly all relevant statistical and mathematical literature where multivariate dependence structures are involved.

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Suchergebnisse - Der eBook Shop

Dependence Modeling: Vine Copula Handbook von: Kurowicka Dorota Kurowicka Verlag:

Dependence Modeling | De Gruyter

Dependence Modeling aims to provide a medium for exchanging results and ideas in the area of multivariate dependence modeling. Topics include Copula methods, environmental sciences, estimation and goodness-of-fit tests, extreme-value theory, limit laws, mass transportations, measures of association, multivariate distributions and tests, quantitative risk management, risk assessment, risk ...

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Uncertainty Analysis with High Dimensional Dependence Modelling von: Dorota Kurowicka, Roger M. Cooke Verlag:

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Pytania do firmy Biedroneczka Dorota Kurowicka Justyna ...

Pytania do firmy Biedroneczka Dorota Kurowicka Justyna Depczyńska Barbara Frączek sp.j. Warszawa

Generating random correlation matrices based on vines and ...

5. Conclusions. The main goal of this paper was to study and improve existing methods of generating random correlation matrices. Two of such methods include the onion method of Ghosh and Henderson and the vine method recently proposed by Joe .Originally the vine method was based on the so-called D-vine.We extend this methodology to any regular vine with studying also the relationship between ...

Biedroneczka - Dorota Kurowicka, Justyna Depczyńska, | Crefo

Biedroneczka - Dorota Kurowicka, Justyna Depczyńska, Barbara Frączek - sp. j., ul. Plutonu Torpedy 14, 02-495 Warszawa, Przedszkola.

dblp: Roger M. Cooke

Dorota Kurowicka, Roger M. Cooke: Advanced input modeling: the vine copula method for representing high dimensional dependent distributions: application to continuous belief nets. Winter Simulation Conference 2002: 270-278

Recent Publications - Lehrstuhl für Mathematische Statistik

Handbook on Vines, Editors: Dorota Kurowicka and Harry Joe, World Scientific. Gräler, B. and Pebesma, E. (2011) The pair-copula construction for spatial data: a new approach to model spatial dependency. Procedia Environmental Sciences 7, 206-211. 2010. Haff, I. H., K. Aas, and A. Frigessi (2010). On the simplified pair-copula construction – simply useful or too simplistic? Journal of ...

Gäste - Lehrstuhl für Mathematische Statistik

2020 Karoline Bax University of Trento, IT 1. Oktober 2020 - 30. September 2021 Raum: BC1 2.01.13, Tel.: 17445. Prof. Ingrid Van Keilegom KU Leuven, BE

Dependence Modeling: Vine Copula Handbook - ISBN ...

Dependence Modeling: Vine Copula Handbook - ISBN: 9789814465236 - (ebook) - von Kurowicka Dorota Kurowicka, Verlag: World Scientific Publishing

Dorota Kurowicka (Author of Uncertainty Analysis with High ...

Dorota Kurowicka is the author of Uncertainty Analysis with High Dimensional Dependence Modelling (4.00 avg rating, 2 ratings, 0 reviews, published 2006)...

Uncertainty Analysis with High Dimensional Dependence ...

Mathematical models are used to simulate complex real-worldphenomena in many areas of science and technology. Large complexmodels typically require inputs whose values are not known withcertainty.

Workshop Vine Copulas - Department - News & Events - TUM ...

Dorota Kurowicka, TU Delft; The topics of the talks will be announced soon. Conference dinner. The conference dinner will take place at Garchinger Augustiner on 8 July 2019 at 18:30. Information on organization. We look forward to your participation! Do you want to report about your research topic? Then apply with your abstract for a 20-minute presentation or poster session. For more ...

Dependence Modeling: Vine Copula Handbook von Dorota ...

Dependence Modeling: Vine Copula Handbook von Dorota Kurowicka, Harry Joe (ISBN 978-981-4299-87-9) bestellen. Schnelle Lieferung, auch auf Rechnung - lehmanns.de

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Группа авторов Uncertainty Modeling in Dose Response


A valuable guide to understanding the problem of quantifying uncertainty in dose response relations for toxic substances In today's scientific research, there exists the need to address the topic of uncertainty as it pertains to dose response modeling. Uncertainty Modeling in Dose Response is the first book of its kind to implement and compare different methods for quantifying the uncertainty in the probability of response, as a function of dose. This volume gathers leading researchers in the field to properly address the issue while communicating concepts from diverse viewpoints and incorporating valuable insights. The result is a collection that reveals the properties, strengths, and weaknesses that exist in the various approaches to bench test problems. This book works with four bench test problems that were taken from real bioassay data for hazardous substances currently under study by the United States Environmental Protection Agency (EPA). The use of actual data provides readers with information that is relevant and representative of the current work being done in the field. Leading contributors from the toxicology and risk assessment communities have applied their methods to quantify model uncertainty in dose response for each case by employing various approaches, including Benchmark Dose Software methods, probabilistic inversion with isotonic regression, nonparametric Bayesian modeling, and Bayesian model averaging. Each chapter is reviewed and critiqued from three professional points of view: risk analyst/regulator, statistician/mathematician, and toxicologist/epidemiologist. In addition, all methodologies are worked out in detail, allowing readers to replicate these analyses and gain a thorough understanding of the methods. Uncertainty Modeling in Dose Response is an excellent book for courses on risk analysis and biostatistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for risk assessment, toxicology, biostatistics, and environmental chemistry professionals who wish to expand their knowledge and expertise in statistical dose response modeling problems and approaches.

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Terje Aven Risk Analysis


Risk Analysis, Second Edition Terje Aven, University of Stavanger, Norway A practical guide to the varied challenges presented in the ever-growing field of risk analysis. Risk Analysis presents an accessible and concise guide to performing risk analysis, in a wide variety of field, with minimal prior knowledge required. Forming an ideal companion volume to Aven's previous Wiley text Foundations of Risk Analysis, it provides clear recommendations and guidance in the planning, execution anduse of risk analysis. This new edition presents recent developments related to risk conceptualization, focusing on related issues on risk assessment and their application. New examples are also featured to clarify the reader's understanding in the application of risk analysis and the risk analysis process. Key features: Fully updated to include recent developments related to risk conceptualization and related issues on risk assessments and their applications. Emphasizes the decision making context of risk analysis rather than just computing probabilities Demonstrates how to carry out predictive risk analysis using a variety of case studies and examples. Written by an experienced expert in the field, in a style suitable for both industrial and academic audiences. This book is ideal for advanced undergraduates, graduates, analysts and researchers from statistics, engineering, finance, medicine and physical sciences. Managers facing decision making problems involving risk and uncertainty will also benefit from this book.

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Professor Caers Jef Modeling Uncertainty in the Earth Sciences


Modeling Uncertainty in the Earth Sciences highlights the various issues, techniques and practical modeling tools available for modeling the uncertainty of complex Earth systems and the impact that it has on practical situations. The aim of the book is to provide an introductory overview which covers a broad range of tried-and-tested tools. Descriptions of concepts, philosophies, challenges, methodologies and workflows give the reader an understanding of the best way to make decisions under uncertainty for Earth Science problems. The book covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of 'engineering' the Earth; uncertainty in the modeling and decision process. Focusing on reliable and practical methods this book provides an invaluable primer for the complex area of decision making with uncertainty in the Earth Sciences.

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Группа авторов Risk Analysis


Everyday we face decisions that carry an element of risk and uncertainty. The ability to analyze, predict, and prepare for the level of risk entailed by these decisions is, therefore, one of the most constant and vital skills needed for analysts, scientists and managers. Risk analysis can be defined as a systematic use of information to identify hazards, threats and opportunities, as well as their causes and consequences, and then express risk. In order to successfully develop such a systematic use of information, those analyzing the risk need to understand the fundamental concepts of risk analysis and be proficient in a variety of methods and techniques. Risk Analysis adopts a practical, predictive approach and guides the reader through a number of applications. Risk Analysis: Provides an accessible and concise guide to performing risk analysis in a wide variety of fields, with minimal prior knowledge required. Adopts a broad perspective on risk, with focus on predictions and highlighting uncertainties beyond expected values and probabilities, allowing a more flexible approach than traditional statistical analysis. Acknowledges that expected values and probabilities could produce poor predictions – surprises may occur. Emphasizes the planning and use of risk analyses, rather than just the risk analysis methods and techniques, including the statistical analysis tools. Features many real-life case studies from a variety of applications and practical industry problems, including areas such as security, business and economy, transport, oil & gas and ICT (Information and Communication Technology). Forms an ideal companion volume to Aven’s previous Wiley text Foundations of Risk Analysis. Professor Aven’s previous book Foundations of Risk Analysis presented and discussed several risk analysis approaches and recommended a predictive approach. This new text expands upon this predictive approach, exploring further the risk analysis principles, concepts, methods and models in an applied format. This book provides a useful and practical guide to decision-making, aimed at professionals within the risk analysis and risk management field.

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