Abstract This paper outlines a Bayesian framework for structured expert judgement ( sej ) that can be utilised as an alternative to the traditional non-Bayesian methods (including the commonly used Cooke's Classical model [13] ). We provide an overview of the structure of an expert judgement study and outline opinion pooling techniques noting the benefits and limitations of these approaches. Some new tractable Bayesian models are highlighted, before presenting our own model which aims to combine and enhance the best of these existing Bayesian frameworks. In particular: clustering, calibrating and then aggregating the experts' judgements utilising a Supra-Bayesian parameter updating approach combined with either an agglomerative hierarchical clustering or an embedded Dirichlet process mixture model. We illustrate the benefit of our approach by analysing data from a number of existing studies in the healthcare domain, specifically in the two contexts of health insurance and transmission risks for chronic wasting disease.

中文翻译：

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一种用于校准和聚合专家判断的贝叶斯方法

摘要 本文概述了用于结构化专家判断 (sej) 的贝叶斯框架，该框架可用作传统非贝叶斯方法（包括常用的库克经典模型 [13]）的替代方法。我们概述了专家判断研究的结构，并概述了意见汇集技术，指出了这些方法的优点和局限性。在展示我们自己的模型之前，重点介绍了一些新的易处理的贝叶斯模型，该模型旨在结合和增强这些现有贝叶斯框架的最佳优势。特别是：使用超贝叶斯参数更新方法与凝聚层次聚类或嵌入式 Dirichlet 过程混合模型相结合，对专家的判断进行聚类、校准和聚合。