Sensitivity analysis in Gaussian Bayesian networks using a divergence measure

被引：16
|
作者：
Gomez-Villegas, Miguel A. [1 ]
Main, Paloma
Susi, Rosario
机构：
[1] Univ Complutense Madrid, Dept Stat & Operat Res, Madrid, Spain
[2] Univ Complutense Madrid, Dept Stat & Operat Res 3, Madrid, Spain
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2007年 / 36卷 / 1-4期
关键词：
Gaussian Bayesian network; Kullback-Leibler divergence; sensitivity analysis;
D O I：
10.1080/03610920600853282
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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.
引用
收藏
页码：523 / 539
页数：17
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