Probabilistic Risk Analysis And Bayesian Decision Theory

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Probabilistic Risk Analysis And Bayesian Decision Theory

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Author : Marcel van Oijen
language : en
Publisher: Springer Nature
Release Date : 2022-11-23

Probabilistic Risk Analysis And Bayesian Decision Theory written by Marcel van Oijen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-23 with Mathematics categories.

The book shows how risk, defined as the statistical expectation of loss, can be formally decomposed as the product of two terms: hazard probability and system vulnerability. This requires a specific definition of vulnerability that replaces the many fuzzy definitions abounding in the literature. The approach is expanded to more complex risk analysis with three components rather than two, and with various definitions of hazard. Equations are derived to quantify the uncertainty of each risk component and show how the approach relates to Bayesian decision theory. Intended for statisticians, environmental scientists and risk analysts interested in the theory and application of risk analysis, this book provides precise definitions, new theory, and many examples with full computer code. The approach is based on straightforward use of probability theory which brings rigour and clarity. Only a moderate knowledge and understanding of probability theory is expected from the reader.

Bayesian Compendium

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Author : Marcel van Oijen
language : en
Publisher: Springer
Release Date : 2024-09-25

Bayesian Compendium written by Marcel van Oijen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-25 with Mathematics categories.

This book describes how Bayesian methods work. Aiming to demystify the approach, it explains how to parameterize and compare models while accounting for uncertainties in data, model parameters and model structures. Bayesian thinking is not difficult and can be used in virtually every kind of research. How exactly should data be used in modelling? The literature offers a bewildering variety of techniques (Bayesian calibration, data assimilation, Kalman filtering, model-data fusion, ...). This book provides a short and easy guide to all these approaches and more. Written from a unifying Bayesian perspective, it reveals how these methods are related to one another. Basic notions from probability theory are introduced and executable R codes for modelling, data analysis and visualization are included to enhance the book’s practical use. The codes are also freely available online. This thoroughly revised second edition has separate chapters on risk analysis and decision theory. It also features an expanded text on machine learning with an introduction to natural language processing and calibration of neural networks using various datasets (including the famous iris and MNIST). Literature references have been updated and exercises with solutions have doubled in number.

Probabilistic Risk Analysis

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Author : Tim Bedford
language : en
Publisher: Cambridge University Press
Release Date : 2001-04-30

Probabilistic Risk Analysis written by Tim Bedford and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-04-30 with Mathematics categories.

Probabilistic risk analysis aims to quantify the risk caused by high technology installations. Increasingly, such analyses are being applied to a wider class of systems in which problems such as lack of data, complexity of the systems, uncertainty about consequences, make a classical statistical analysis difficult or impossible. The authors discuss 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 judgement and uncertainty propagation. The relationship of risk analysis with decision making is highlighted in chapters on influence diagrams and decision theory. Finally, the difficulties of choosing metrics to quantify risk, and current regulatory frameworks are discussed.

Bayesian Decision Analysis

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Author : Jim Q. Smith
language : en
Publisher: Cambridge University Press
Release Date : 2010-09-23

Bayesian Decision Analysis written by Jim Q. Smith and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-23 with Mathematics categories.

Bayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis of simple decision problems to a careful analysis of the sometimes very complex and data rich structures confronted by practitioners. The book contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence diagrams and causal Bayesian networks. The author demonstrates when and how the theory can be successfully applied to a given decision problem, how data can be sampled and expert judgements elicited to support this analysis, and when and how an effective Bayesian decision analysis can be implemented. Evolving from a third-year undergraduate course taught by the author over many years, all of the material in this book will be accessible to a student who has completed introductory courses in probability and mathematical statistics.

Probabilistic Risk Assessment

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Author :
language : en
Publisher:
Release Date : 2000

Probabilistic Risk Assessment written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Reliability (Engineering) categories.

Contains references to documents in the NASA Scientific and Technical (STI) Database.

Decisions Under Uncertainty

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Author : Ian Jordaan
language : en
Publisher: Cambridge University Press
Release Date : 2005-04-07

Decisions Under Uncertainty written by Ian Jordaan and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-04-07 with Technology & Engineering categories.

To better understand the core concepts of probability and to see how they affect real-world decisions about design and system performance, engineers and scientists might want to ask themselves the following questions: what exactly is meant by probability? What is the precise definition of the 100-year load and how is it calculated? What is an 'extremal' probability distribution? What is the Bayesian approach? How is utility defined? How do games fit into probability theory? What is entropy? How do I apply these ideas in risk analysis? Starting from the most basic assumptions, this 2005 book develops a coherent theory of probability and broadens it into applications in decision theory, design, and risk analysis. This book is written for engineers and scientists interested in probability and risk. It can be used by undergraduates, graduate students, or practicing engineers.

Non Bayesian Decision Theory

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Author : Martin Peterson
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-06-06

Non Bayesian Decision Theory written by Martin Peterson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-06 with Science categories.

For quite some time, philosophers, economists, and statisticians have endorsed a view on rational choice known as Bayesianism. The work on this book has grown out of a feeling that the Bayesian view has come to dominate the academic com- nitytosuchanextentthatalternative,non-Bayesianpositionsareseldomextensively researched. Needless to say, I think this is a pity. Non-Bayesian positions deserve to be examined with much greater care, and the present work is an attempt to defend what I believe to be a coherent and reasonably detailed non-Bayesian account of decision theory. The main thesis I defend can be summarised as follows. Rational agents m- imise subjective expected utility, but contrary to what is claimed by Bayesians, ut- ity and subjective probability should not be de?ned in terms of preferences over uncertain prospects. On the contrary, rational decision makers need only consider preferences over certain outcomes. It will be shown that utility and probability fu- tions derived in a non-Bayesian manner can be used for generating preferences over uncertain prospects, that support the principle of maximising subjective expected utility. To some extent, this non-Bayesian view gives an account of what modern - cision theory could have been like, had decision theorists not entered the Bayesian path discovered by Ramsey, de Finetti, Savage, and others. I will not discuss all previous non-Bayesian positions presented in the literature.

Bayesian Compendium

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Author : Marcel van Oijen
language : en
Publisher: Springer Nature
Release Date : 2024-08-27

Bayesian Compendium written by Marcel van Oijen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-27 with Mathematics categories.

This book describes how Bayesian methods work. Aiming to demystify the approach, it explains how to parameterize and compare models while accounting for uncertainties in data, model parameters and model structures. Bayesian thinking is not difficult and can be used in virtually every kind of research. How exactly should data be used in modelling? The literature offers a bewildering variety of techniques (Bayesian calibration, data assimilation, Kalman filtering, model-data fusion, ...). This book provides a short and easy guide to all these approaches and more. Written from a unifying Bayesian perspective, it reveals how these methods are related to one another. Basic notions from probability theory are introduced and executable R codes for modelling, data analysis and visualization are included to enhance the book’s practical use. The codes are also freely available online. This thoroughly revised second edition has separate chapters on risk analysis and decision theory. It also features an expanded text on machine learning with an introduction to natural language processing and calibration of neural networks using various datasets (including the famous iris and MNIST). Literature references have been updated and exercises with solutions have doubled in number.

Uncertainty In Risk Assessment Risk Management And Decision Making

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Author : V.T. Covello
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-08

Uncertainty In Risk Assessment Risk Management And Decision Making written by V.T. Covello and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-08 with Technology & Engineering categories.

The subject of this volume--uncertainties in risk assessment and management--reflects an important theme in health, safety, and environ mental decision making. MOst technological hazards are characterized by substantial uncertainty. Recent examples include nuclear waste disposal, acid rain, asbestos in schools, carcinogens in food, and hazardous waste. realing with such uncertainty is arguably the most difficult and challeng ing task facing risk assessors and managers today. Four primary sources of uncertainty in risk assessment and management can be identified: (1) uncertainties about definitions; (2) uncertainties about scientific facts; (3) uncertainties about risk perceptions and atti tudes; and (4) uncertainties about values. Uncertainties about definitions derive primarily from disagreements about the meaning and interpretation of key concepts, such as probability. Uncertainties about scientific facts derive primarily from disagreements about failure modes, the probability and magnitude of adverse health or environmental consequences, cause and effect relationships, dose-response relationships, and exposure patterns. Uncertainties about risk perceptions and attitudes derive primarily from disagreements about what constitutes a significant or acceptable level of risk. Uncertainties about values derive primarily from disagreements about the desirability or worth of alternative risk management actions or conse quences. The papers in this volume address each of these sources of uncertainty from a variety of perspectives. Reflecting the broad scope of risk assess ment and risk management research, the papers include contributions from safety engineers, epidemiologists, toxicologists, chemists, biostatisticians, biologists, decision analysts, economists,psychologists, political scien tists, sociologists, ethicists, and lawyers.

Adversarial Risk Analysis

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Author : David L. Banks
language : en
Publisher: CRC Press
Release Date : 2015-06-30

Adversarial Risk Analysis written by David L. Banks and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-30 with Business & Economics categories.

Winner of the 2017 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)A relatively new area of research, adversarial risk analysis (ARA) informs decision making when there are intelligent opponents and uncertain outcomes. Adversarial Risk Analysis develops methods for allocating defensive or offensive resources against