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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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

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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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