为什么使用置信网络的诊断对概率的不精确性不敏感？
Why Is Diagnosis Using Belief Networks Insensitive to Imprecision In Probabilities?
摘要：
最近的研究发现，贝叶斯信念网络的诊断性能往往是令人惊讶的不敏感的数值概率的不精确性。例如，作者最近完成了一项广泛的研究，他们将随机噪声应用于一组用于医疗诊断的信念网络中的数值概率，CPCS网络的子集，QMR（快速医疗参考）的子集，专注于肝脏和胆汁疾病。根据分配给实际疾病的平均概率，诊断性能即使对大量噪声也表现出很小的灵敏度。在本文中，我们总结了这项研究的结果，并讨论了这种低敏感性的可能解释。一个原因是性能的标准是真实假设的平均概率，而不是概率的平均误差，这对对称噪声分布不敏感。但是，我们表明，即使是不对称的，loxos正常的噪音有适度的影响。第二个原因是黄金标准的后验概率通常接近于零或一，并且几乎不受噪声的干扰。
英文摘要：
Recent research has found that diagnostic performance with Bayesian belief networks is often surprisingly insensitive to imprecision in the numerical probabilities. For example, the authors have recently completed an extensive study in which they applied random noise to the numerical probabilities in a set of belief networks for medical diagnosis, subsets of the CPCS network, a subset of the QMR (Quick Medical Reference) focused on liver and bile diseases. The diagnostic performance in terms of the average probabilities assigned to the actual diseases showed small sensitivity even to large amounts of noise. In this paper, we summarize the findings of this study and discuss possible explanations of this low sensitivity. One reason is that the criterion for performance is average probability of the true hypotheses, rather than average error in probability, which is insensitive to symmetric noise distributions. But, we show that even asymmetric, logodds-normal noise has modest effects. A second reason is that the gold-standard posterior probabilities are often near zero or one, and are little disturbed by noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
