Probabilistic risk analysis aims to quantify the risk caused by high technology installations in situations where classical statistical analysis is difficult or impossible. This book discusses the fundamental notion of uncertainty, its relationship with probability, and the limits to the quantification of uncertainty. Drawing on extensive experience in the theory and applications of risk analysis, the authors focus on the conceptual and mathematical foundations underlying the quantification, interpretation and management of risk. They cover standard topics as well as important new subjects such as the use of expert judgment and uncertainty propagation.
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"This is a valuable reference book for engineers, researchers and Ph.D. students...The book is fresh, neat, and comprehensive. I highly recommend it." Risk Decision and Policy
"...an excellent basis for an undergraduate course, well equipped with a wealth of real-life examples and exercises. Practitioners and decision makers will also benefit...The text can be recommended as an important contribution to bridge the gap between those working in mathematical reliability, who are looking for real-life examples, and those in eonomic risk analysis, who are looking for a readable treatment of the foundational concepts." JASA
"...an excellent basis for an undergraduate course, well equipped with a wealth of real-life examples and exercises. Practitioners and decision makers will also benefit...The text can be recommended as an important contribution to bridge the gap between those working in mathematical reliability, who are looking for real-life examples, and those in eonomic risk analysis, who are looking for a readable treatment of the foundational concepts." JASA
Book Description
Probabilistic risk analysis is applied in many areas of engineering. This book focuses on the conceptual and mathematical foundations of risk analysis. It differs from other books in this area first by discussing the meaning of uncertainty, and the extent to which one can use mathematical tools to model uncertainty. Second, a number of new modelling techniques are described. Finally, there is a good discussion of methods for quantifying the parameters in mathematical models, even where there is no 'classical' data available, by the use of expert judgement.
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This review is from: Probabilistic Risk Analysis: Foundations and Methods (Hardcover)
A very clear text, what is not usual in this kind of book.
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Inside This Book
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First Sentence:
Probabilistic risk analysis (PRA), also called quantitative risk analysis (QRA) or probabilistic safety analysis (PSA), is currently being widely applied to many sectors, including transport, construction, energy, chemical processing, aerospace, the military, and even to project planning and financial management. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
weighting factors model, given rank correlation, independence preservation property, elicitation variables, probabilistic inversion, subsurvival functions, service sojourn, sneak analysis, mutual preferential independence, cobweb plot, exponential life distribution, standard vine, reliability data bases, correlation tree, calibration scores, intrinsic range, independent competing risks, expected disutility, utility independence, failure rate model, probabilistic risk analysis, calibration variables, expert distributions, old turbine, proper scoring rule Key Phrases - Capitalized Phrases (CAPs): (learn more)
Reactor Safety Study, Monte Carlo, New Waterway, Quantile Median, Three Mile Island, Effective Ineffective New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Probabilistic risk analysis (PRA), also called quantitative risk analysis (QRA) or probabilistic safety analysis (PSA), is currently being widely applied to many sectors, including transport, construction, energy, chemical processing, aerospace, the military, and even to project planning and financial management. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
weighting factors model, given rank correlation, independence preservation property, elicitation variables, probabilistic inversion, subsurvival functions, service sojourn, sneak analysis, mutual preferential independence, cobweb plot, exponential life distribution, standard vine, reliability data bases, correlation tree, calibration scores, intrinsic range, independent competing risks, expected disutility, utility independence, failure rate model, probabilistic risk analysis, calibration variables, expert distributions, old turbine, proper scoring rule Key Phrases - Capitalized Phrases (CAPs): (learn more)
Reactor Safety Study, Monte Carlo, New Waterway, Quantile Median, Three Mile Island, Effective Ineffective New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 57
books:
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6
books
cite this book:
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- Fault Tree Handbook by William E. Vesely on 4 pages
- Nuclear Power by Richard S. Lewis on page 7, and Back Matter
- Nuclear Power (20th Century Inventions) by Nina Morgan in Back Matter (1), and Back Matter (2)
- Introduction to Bayesian Networks by Finn V. Jensen in Back Matter
- Software Reliability Modelling and Identification (Lecture Notes in Computer Science) by Sergio Bittanti in Back Matter
See all 57 books this book cites
- Modern Statistical And Mathematical Methods in Reliability (Quality, Reliability and Engineering Statistics) by Sallie Keller-McNulty on page 16, page 26, and page 118
- Modern Information Processing: From Theory to Applications by Bernadette Bouchon-Meunier on page 229
- System Reliability Theory: Models, Statistical Methods, and Applications, 2nd Edition (Wiley Series in Probability and Statistics) by Marvin Rausand in Back Matter
- Classical Competing Risks by Martin J. Crowder in Back Matter
- On Risk and Disaster: Lessons from Hurricane Katrina by Ronald J. Daniels in Back Matter
See all 6 books citing this book
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