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"This book provides a comprehensive demonstration of risk analysis as a distinct science covering risk understanding, assessment, perception, communication, management, governance, and policy. It presents and discusses the key pillars of this science, and provides guidance on how to conduct high-quality risk analysis. The Science of Risk Analysis seeks to strengthen risk analysis as a field and science by summarising and extending current work on the topic. It presents the foundation for a distinct risk field and science based on recent research, and explains the difference between applied risk analysis (to provide risk knowledge and tackle risk problems in relation to for example medicine, engineering, business or climate change) and generic risk analysis (on concepts, theories, frameworks, approaches, principles, methods and models to understand, assess, characterise, communicate, manage and govern risk). The book clarifies and describes key risk science concepts, and builds on recent foundational work conducted by the Society for Risk Analysis in order to provide new perspectives on science and risk analysis. The topics covered are accompanied by cases and examples relating to current issues throughout. This book is essential reading for risk analysis professionals, scientists, students and practitioners, and will also be of interest to scientists and practitioners from other fields who apply risk analysis in their work"--

"There are a growing number of researchers and analysts who find the probability-based approaches for assessing risk and uncertainties to be too narrow and limiting. Uncertainty in Risk Assessment provides a broad conceptual framework and describes various alternative approaches of uncertainty representation and characterization therein such as probability-bound analysis, imprecise probability and evidence theory. The authors, whose own research has been at the forefront of developments in the field, include a number of real-life applications which demonstrate the practical use of the various methods in the different realistic circumstances. They provide invaluable practical guidance and clear recommendations on how and when to use the various approaches"--

"There are a growing number of researchers and analysts who find the probability-based approaches for assessing risk and uncertainties to be too narrow and limiting. Uncertainty in Risk Assessment provides a broad conceptual framework and describes various alternative approaches of uncertainty representation and characterization therein such as probability-bound analysis, imprecise probability and evidence theory. The authors, whose own research has been at the forefront of developments in the field, include a number of real-life applications which demonstrate the practical use of the various methods in the different realistic circumstances. They provide invaluable practical guidance and clear recommendations on how and when to use the various approaches"--

"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"--

Conception and Misconceptions about Risk looks at the fundamental issues in risk analysis and risk management, providing recommendations and guidance. This book presents the general thinking process related to the understanding of risk, and how we should describe, analyse, evaluate, manage, and communicate risk. In this book a number of important conceptions and misconceptions about risk have been identified their implications for risk analysis and risk management are discussed. The key statistical concepts are introduced and discussed thoroughly, however a minimum background in statistics, ri.

1 Introduction to Reliability and Risk Analysis -- 1.1 Historical perspective -- 1.2 Objectives of reliability and risk analyses -- 1.3 Subject areas. Modelling -- 1.4 Basic concepts -- 1.5 Planning, execution and use of reliability analysis -- 1.6 Planning, execution and use of risk analysis -- 1.7 Discussion -- 1.8 Problems -- 2 Basic Reliability and Risk Analysis Methods -- 2.1 Use of statistics -- 2.2 Failure mode and effect analysis -- 2.3 Fault tree analysis -- 2.4 Cause consequence analysis/Event tree analysis -- 2.5 Other reliability and risk analysis methods -- 2.6 Problems -- 3 Binary Models for Analysing System Reliability -- 3.1 Structural properties -- 3.2 Computing system reliability -- 3.3 Non-repairable systems -- 3.4 Repairable systems -- 3.5 Reliability importance measures -- 3.6 Dependent components -- 3.7 Problems -- 4 Multistate Models for Analysing System Reliability -- 4.1 Multistate monotone systems -- 4.2 Computing system reliability -- 4.3 A time dependent model -- 4.4 Discussion -- 4.5 Problems -- 5 Optimal Replacement Policies -- 5.1 Introduction -- 5.2 General minimal repair, age replacement model -- 5.3 Block replacement model -- 5.4 On-condition replacement models -- 5.5 Problems -- 6 Safety Management -- 6.1 Introduction -- 6.2 Safety management in a company -- 6.3 Reliability management of a product -- 6.4 Problems -- 7 Some Applications -- 7.1 Risk analysis of an offshore oil and gas producing platform -- 7.2 Availability and life cycle costing in a gas project -- 7.3 Availability and life cycle costing related to a process instrumentation system -- 7.4 Discussion -- 7.5 Problems -- Appendicies -- A Probability Theory -- A.l Probabilities -- A.1.1 Probability concepts -- A.1.2 Rules for combining probabilities -- A.1.3 Conditional probabilities -- A.1.4 Independence -- A.2 Stochastic variables -- A.3 Some proofs -- A.3.1 Proof of formula (A.4) -- A.3.2 Probability calculations in event trees -- A.3.3 Proof of an error bound for the approximations (4.2) and (4.3). -- A.4 Problems -- B Stochastic Failure Models -- B.1 Non-repairable units -- B.1.1 Basic concepts -- B.1.2 Some common lifetime distributions -- B.2 Repairable units -- B.3 Binomial distribution -- B.4 Gamma function -- B.5 Problems -- C Statistical Analysis of Reliability Data -- C.1 Identification of lifetime distribution, Hazard plotting -- C.2 Estimation of parameters in lifetime distributions -- C.3 Statistical analysis of non-homogeneous Poisson processes -- C.4 Data sources -- C.5 Problems -- D Markov Models -- D.1 Introduction -- D.2 One component. No repair -- D.3 One repairable component -- D.4 Two repairable components -- D.5 Birth and death processes -- D.6 General formulation of a Markov process -- D.7 Problems -- E More on Stochastic Processes -- E.1 Renewal processes -- E.2 Renewal reward processes -- E.3 Regenerative processes -- E.4 Counting processes -- E.5 Proofs of some results in Chapters 3 and 5 -- E.5.1 Section 3.4.1 -- E.5.2 Section 3.4.2 -- E.5.3 Section 5.4 -- F An Algorithm for Calculating System Reliability -- G A Minimization Technique.