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Bayesian networks of customer satisfaction survey data

机译：贝叶斯客户满意度调查数据网络

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A Bayesian network (BN) is a probabilistic graphical model that represents a set of variables and their probabilistic dependencies. Formally, BNs are directed acyclic graphs whose nodes represent variables, and whose arcs encode the conditional dependencies among the variables. Nodes can represent any kind of variable, be it a measured parameter, a latent variable, or a hypothesis. They are not restricted to represent random variables, which form the "Bayesian" aspect of a BN. Efficient algorithms exist that perform inference and learning in BNs. BNs that model sequences of variables are called dynamic BNs. In this context, [A. Harel. R. Kenett, and F. Ruggeri, Modeling web usability diagnostics on the basis of usage statistics, in Statistical Methods in eCommerce Research, W. Jank and G. Shmueli, eds., Wiley, 2008] provide a comparison between Markov Chains and BNs in the analysis of web usability from e-commerce data. A comparison of regression models, structural equation models, and BNs is presented in Anderson et al. [R.D. Anderson, R.D. Mackoy, V.B. Thompson, and G. Harrell, A bayesian network estimation of the service-profit Chain for transport service satisfaction, Decision Sciences 35(4), (2004), pp. 665-689]. In this article we apply BNs to the analysis of customer satisfaction surveys and demonstrate the potential of the approach. In particular, BNs offer advantages in implementing models of cause and effect over other statistical techniques designed primarily for testing hypotheses. Other advantages include the ability to conduct probabilistic inference for prediction and diagnostic purposes with an output that can be intuitively understood by managers.
机译：贝叶斯网络（BN）是一种概率图形模型，它表示一组变量及其概率依存关系。形式上，BN是有向无环图，其节点表示变量，其弧线编码变量之间的条件依存关系。节点可以表示任何种类的变量，无论是测量参数，潜在变量还是假设。它们不限于代表随机变量，它们构成了BN的“贝叶斯”方面。存在在BN中执行推理和学习的高效算法。建模变量序列的BN称为动态BN。在这种情况下，[A。哈雷尔R. Kenett和F.Ruggeri，“在使用统计基础上对Web可用性诊断进行建模”，《电子商务研究的统计方法》，W。Jank和G.Shmueli编辑，Wiley，2008年]提供了Markov Chains和BN之间的比较。从电子商务数据分析Web可用性。 Anderson等人对回归模型，结构方程模型和BN进行了比较。 [R.D.安德森（R.D. Mackoy），V.B. Thompson和G. Harrell，服务利润链对运输服务满意度的贝叶斯网络估计，决策科学35（4），（2004年），第665-689页。在本文中，我们将BN应用到客户满意度调查的分析中，并证明该方法的潜力。特别是，与其他主要用于检验假设的统计技术相比，BN在实施因果模型方面具有优势。其他优点包括能够进行预测和诊断目的的概率推断，并具有管理者可以直观理解的输出。

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