Sensitivity analysis in Gaussian Bayesian networks using a divergence measure
Sensitivity analysis in Gaussian Bayesian networks using a divergence measure
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DOI：
10.1080/03610920600853282
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发表时间：
2007-01-01
影响因子：
0.8
通讯作者：
Susi, Rosario
中科院分区：
文献类型：
--
作者：
Gomez-Villegas, Miguel A.;Main, Paloma;Susi, Rosario
本文提出了一种计算高斯贝叶斯网络灵敏度分析的方法。该方法基于Kullback-Leibler散度，可用于评估网络中目标变量的后验边缘密度对先验变化的影响。我们发现，一些变化并不干扰感兴趣的后缘密度。最后，我们描述了一种方法来比较不同的敏感度度量，这些度量依赖于不准确的地方。并用一个实例说明了所提出的概念和方法。
This article develops a method for computing the sensitivity analysis in a Gaussian Bayesian network. The measure presented is based on the Kullback-Leibler divergence and is useful to evaluate the impact of prior changes over the posterior marginal density of the target variable in the network. We find that some changes do not disturb the posterior marginal density of interest. Finally, we describe a method to compare different sensitivity measures obtained depending on where the inaccuracy was. An example is used to illustrate the concepts and methods presented.