学习和解释不对称标记的DAGs：对COVID-19恐惧的案例研究
Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
摘要：
贝叶斯网络被广泛用于学习和推理离散变量的依赖结构。然而，它们只能形式化地编码对称条件独立性，这在实践中往往过于严格而无法保持。最近提出了一种基于贝叶斯标记的DAG，它通过放松独立性的对称假设来扩展贝叶斯网络的类，并表示感兴趣的变量之间存在的依赖类型。在这里，我们介绍了新的结构学习算法这一类的模型，同时是有效的，允许一个简单的解释底层的依赖结构。一个全面的计算研究突出了算法的效率。一个真实世界的数据应用程序使用了在意大利收集的COVID-19恐惧量表的数据，展示了它们在实践中的应用。
英文摘要：
Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables. However, they are only capable of formally encoding symmetric conditional independence, which in practice is often too strict to hold. Asymmetry-labeled DAGs have been recently proposed to both extend the class of Bayesian networks by relaxing the symmetric assumption of independence and denote the type of dependence existing between the variables of interest. Here, we introduce novel structural learning algorithms for this class of models which, whilst being efficient, allow for a straightforward interpretation of the underlying dependence structure. A comprehensive computational study highlights the efficiency of the algorithms. A real-world data application using data from the Fear of COVID-19 Scale collected in Italy showcases their use in practice.
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