Evidence Propagation and Value of Evidence on Influence Diagrams
Evidence Propagation and Value of Evidence on Influence Diagrams
复制标题
影响力图上的证据传播和证据价值
DOI：
10.1287/opre.46.1.73
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
K. Ezawa
中科院分区：
文献类型：
--
作者：
K. Ezawa
在本文中，我们介绍了影响图的证据传播操作，证据的价值来衡量新的观察/实验的影响/价值的概念，以及启示的价值的概念。在基于影响图（广义贝叶斯网络）范式的规范化专家系统中，证据传播操作对于证据值的计算、一般更新和推理操作至关重要。证据价值允许我们计算结果敏感度，直接定义为证据价值之间的最大差异，而完美信息的价值则是证据价值的期望值。我们将证据的揭示价值定义为证据价值中的最优价值。我们讨论了揭示价值和控制价值之间的关系。我们还讨论了与证据价值和完美信息价值的计算有关的实施问题。
In this paper, we introduce evidence propagation operations on influence diagrams, a concept of the value of evidence to measure the impact/value of new observations/experimentation, and a concept of the value of revelation. Evidence propagation operations are critical for the computation of the value of evidence, general update and inference operations in normative expert systems that are based on the influence diagram (generalized Bayesian network) paradigm. The value of evidence allows us to compute the outcome sensitivity directly defined as the maximum difference among the values of evidence, and the value of perfect information, as the expected value of the values of evidence. We define the value of revelation as the optimal value of the values of evidence. We discuss the relationship between the value of revelation and the value of control. We also discuss implementation issues related to computation of the value of evidence and the value of perfect information.