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Ranking evaluation of institutions based on a Bayesian network having a latent variable

机译：基于具有潜在变量的贝叶斯网络的机构排名评估

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This paper proposes a new probabilistic graphical model which contains an unobservable latent variable that affects all other observable variables, and the proposed model is applied to ranking evaluation of institutions using a set of performance indicators. Linear Gaussian models are used to express the causal relationship among variables. The proposed iterative method uses a combined causal discovery algorithm of score-based and constraint-based methods to find the network structure, while Gibbs sampling and regression analysis are conducted to estimate the parameters. The latent variable representing ranking scores of institutions is estimated, and the rankings are determined by comparing the estimated scores. The interval estimate of the ranking of an institution is finally obtained from a repetitive procedure. The proposed procedure was applied to a real data set as well as artificial data sets.
机译：本文提出了一个新的概率图形模型，该模型包含一个影响所有其他可观察变量的不可观察潜在变量，并将该模型应用于使用一组绩效指标对机构进行排名评估。线性高斯模型用于表达变量之间的因果关系。所提出的迭代方法使用基于分数和基于约束的方法的组合因果发现算法来查找网络结构，同时进行Gibbs采样和回归分析以估计参数。估计代表机构排名分数的潜在变量，并通过比较估算分数确定排名。机构排名的区间估计最终是从重复过程中获得的。拟议的程序被应用于实际数据集以及人工数据集。

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