Balancing the Elicitation Burden and the Richness of Expert Input When Quantifying Discrete Bayesian Networks
Balancing the Elicitation Burden and the Richness of Expert Input When Quantifying Discrete Bayesian Networks
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DOI：
10.1111/risa.13772
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发表时间：
2021-06-19
期刊：
影响因子：
3.8
通讯作者：
Hanea, Anca M.
中科院分区：
文献类型：
--
作者：
Barons, Martine J.;Mascaro, Steven;Hanea, Anca M.
结构化专家判断（SEJ）是一种以结构化的方式从专家群体中获得不确定数量的估计的方法，旨在最大限度地减少非结构化方法的普遍认知弱点。当所需的数量很大时，专家组的负担就很重，资源限制可能意味着不可能引起所有感兴趣的数量。部分推导可以用对剩余的未推导量的归算方法加以补充。如果感兴趣的数量是条件概率分布，则可以利用数量之间的自然关系来推算缺失概率。在这里，我们测试贝叶斯智能插值方法及其对贝叶斯网络条件概率表的变化，称为“InterBeta”。我们比较了InterBeta在两种情况下的各种输出，其中条件概率表是从专家组中得出的。我们表明插值值与专家的值很好地一致，并指导如何使用InterBeta来更好地减少专家在SEJ练习中的负担。
Structured expert judgment (SEJ) is a method for obtaining estimates of uncertain quantities from groups of experts in a structured way designed to minimize the pervasive cognitive frailties of unstructured approaches. When the number of quantities required is large, the burden on the groups of experts is heavy, and resource constraints may mean that eliciting all the quantities of interest is impossible. Partial elicitations can be complemented with imputation methods for the remaining, unelicited quantities. In the case where the quantities of interest are conditional probability distributions, the natural relationship between the quantities can be exploited to impute missing probabilities. Here we test the Bayesian intelligence interpolation method and its variations for Bayesian network conditional probability tables, called "InterBeta." We compare the various outputs of InterBeta on two cases where conditional probability tables were elicited from groups of experts. We show that interpolated values are in good agreement with experts' values and give guidance on how InterBeta could be used to good effect to reduce expert burden in SEJ exercises.