Facilitating the elicitation of beliefs for use in Bayesian Belief modelling
Facilitating the elicitation of beliefs for use in Bayesian Belief modelling
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DOI：
10.1016/j.envsoft.2019.104539
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发表时间：
2019-12-01
影响因子：
4.9
通讯作者：
Whitmore, Andrew P.
中科院分区：
文献类型：
--
作者：
Hassall, Kirsty L.;Dailey, Gordon;Whitmore, Andrew P.
专家意见越来越多地被用于通知贝叶斯信念网络，特别是定义由图形结构建模的条件依赖关系。由于所需信息的数量和专家有效量化主观信念的能力，这种专家意见的启发仍然是一个重大挑战。在这项工作中，我们介绍了一种方法，旨在初始化条件概率表的基础上，少量的简单的问题，捕获的整体形状的条件概率分布，然后使专家以一种有效的方式来完善他们的结果。这些方法已被纳入条件概率启发（ACE）软件应用程序中，可在https://github.com/KirstyLHassall/ACE上免费获得（Hassall，2019）。
Expert opinion is increasingly being used to inform Bayesian Belief Networks, in particular to define the conditional dependencies modelled by the graphical structure. The elicitation of such expert opinion remains a major challenge due to both the quantity of information required and the ability of experts to quantify subjective beliefs effectively. In this work, we introduce a method designed to initialise conditional probability tables based on a small number of simple questions that capture the overall shape of a conditional probability distribution before enabling the expert to refine their results in an efficient way. These methods have been incorporated into a software Application for Conditional probability Elicitation (ACE), freely available at https://github.com/KirstyLHassall/ACE (Hassall, 2019).