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Titlebook: Expert Judgement in Risk and Decision Analysis; Anca M. Hanea,Gabriela F. Nane,Simon French Book 2021 Springer Nature Switzerland AG 2021

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发表于 2025-3-21 18:21:55 | 显示全部楼层 |阅读模式
书目名称Expert Judgement in Risk and Decision Analysis
编辑Anca M. Hanea,Gabriela F. Nane,Simon French
视频video
概述Contributions of Roger Cooke and the Classical Model, as well as a section on Training and Education.Based on the COST (European Cooperation in Science & Technology) Action on “Expert Judgement”.Analy
丛书名称International Series in Operations Research & Management Science
图书封面Titlebook: Expert Judgement in Risk and Decision Analysis;  Anca M. Hanea,Gabriela F. Nane,Simon French Book 2021 Springer Nature Switzerland AG 2021
描述This book pulls together many perspectives on the theory, methods and practice of drawing judgments from panels of experts in assessing risks and making decisions in complex circumstances. The book is divided into four parts: Structured Expert Judgment (SEJ) current research fronts; the contributions of Roger Cooke and the Classical Model he developed; process, procedures and education; and applications..After an Introduction by the Editors, the first part presents chapters on expert elicitation of parameters of multinomial models; the advantages of using performance weighting by advancing the “random expert” hypothesis; expert elicitation for specific graphical models; modelling dependencies between experts’ assessments within a Bayesian framework; preventive maintenance optimization in a Bayesian framework; eliciting life time distributions to parametrize a Dirichlet process; and on an adversarial risk analysis approach for structured expert judgment studies..The second part includes Roger Cooke’s oration from 1995 on taking up his chair at Delft University of Technology; one of the editors reflections on the early decade of the Classical Model development and use; a current over
出版日期Book 2021
关键词Structured Expert Judgement (SEJ); Elicitation; Structural Elicitation; Adversarial Risk Analysis (ARA)
版次1
doihttps://doi.org/10.1007/978-3-030-46474-5
isbn_softcover978-3-030-46476-9
isbn_ebook978-3-030-46474-5Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightSpringer Nature Switzerland AG 2021
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发表于 2025-3-21 21:32:46 | 显示全部楼层
Recent Advances in the Elicitation of Uncertainty Distributions from Experts for Multinomial Probabia subjective prior distribution in a Bayesian analysis, for a set of probabilities which are constrained to sum to one. A typical context for this is as a prior distribution for the probabilities in a multinomial model. The Dirichlet distribution has long been advocated as a natural way to represent
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Are Performance Weights Beneficial? Investigating the Random Expert Hypothesisces, combining various expert opinions is an important topic. In the Classical Model, uncertainty distributions for the variables of interest are based on an aggregation of elicited expert percentiles. Aggregation of these expert distributions is accomplished using linear opinion pooling relying on
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Three-Point Lifetime Distribution Elicitation for Maintenance Optimization in a Bayesian Contextost likely estimate in between these quantile estimates are to be elicited, which uniquely determine a member in a flexible family of distributions that is consistent with these estimates. Multiple expert elicited lifetime distributions in this manner are next used to arrive at the prior parameters
发表于 2025-3-22 20:36:11 | 显示全部楼层
Adversarial Risk Analysis as a Decomposition Method for Structured Expert Judgement Modellingast the actions of competitors based on expert knowledge. This is relevant in areas such as cybersecurity, security, defence and business competition. As a consequence, we present a structured approach to facilitate the elicitation of probabilities over the actions of other intelligent agents by dec
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An In-Depth Perspective on the Classical Modelevaluation when aggregating experts’ assessments of uncertain quantities. Assessing experts’ performance in quantifying uncertainty involves two scores in CM, the calibration score (or statistical accuracy) and the information score. The two scores combine into overall scores, which, in turn, yield
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