Bayesian networks (BNs) are graphical probabilistic models used for reasoning under uncertainty. These models are becoming increasing popular in a range of fields including ecology, computational biology, medical diagnosis, and forensics. In most of these cases, the BNs are quantified using information from experts, or from user opinions. An interest therefore lies in the way in which multiple opinions can be represented and used in a BN. This paper proposes the use of a measurement error model to combine opinions for use in the quantification of a BN. The multiple opinions are treated as a realisation of measurement error and the model uses the posterior probabilities ascribed to each node in the BN which are computed from the prior information given by each expert. The proposed model addresses the issues associated with current methods of combining opinions such as the absence of a coherent probability model, the lack of the conditional independence structure of the BN being maintained, and the provision of only a point estimate for the consensus. The proposed model is applied an existing Bayesian Network and performed well when compared to existing methods of combining opinions.

中文翻译：

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结合使用贝叶斯网络的意见：一种测量误差方法

贝叶斯网络 (BN) 是用于在不确定性下进行推理的图形概率模型。这些模型在生态学、计算生物学、医学诊断和法医学等一系列领域越来越受欢迎。在大多数情况下，BN 是使用专家的信息或用户意见来量化的。因此，兴趣在于在 BN 中可以表示和使用多种意见的方式。本文提出了使用测量误差模型来组合意见以用于 BN 的量化。多个意见被视为测量误差的实现，该模型使用归属于 BN 中每个节点的后验概率，这些后验概率是根据每个专家给出的先验信息计算出来的。提议的模型解决了与当前合并意见的方法相关的问题，例如缺乏一致的概率模型、缺乏 BN 的条件独立结构以及仅提供共识的点估计。所提出的模型应用于现有的贝叶斯网络，并且与现有的合并意见的方法相比表现良好。