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Data-Informed Calibration and Aggregation of Expert Judgment in a Bayesian Framework

dc.contributor.advisorMosleh, Alien_US
dc.contributor.authorShirazi, Calvin Homayoonen_US
dc.description.abstractHistorically, decision-makers have used expert opinion to supplement lack of data. Expert opinion, however, is applied with much caution. This is because judgment is subjective and contains estimation error with some degree of uncertainty. The purpose of this study is to quantify the uncertainty surrounding the unknown of interest, given an expert opinion, in order to reduce the error of the estimate. This task is carried out by data-informed calibration and aggregation of expert opinion in a Bayesian framework. Additionally, this study evaluates the impact of the number of experts on the accuracy of aggregated estimate. The objective is to determine the correlation between the number of experts and the accuracy of the combined estimate in order to recommend an expert panel size.en_US
dc.titleData-Informed Calibration and Aggregation of Expert Judgment in a Bayesian Frameworken_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentReliability Engineeringen_US
dc.subject.pqcontrolledEngineering, Mechanicalen_US

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