Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, in this paper we will review how Bayesian networks can model dynamic data and data with incomplete observations. Such data are the norm at the forefront of research and in practical applications, and Bayesian networks are uniquely positioned to model them due to their explainability and interpretability.

中文翻译：

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不完整和动态数据的贝叶斯网络模型

贝叶斯网络是一种多功能且强大的工具，可以以概率原理的方式对复杂现象及其组件的相互作用进行建模。除了在文献中受到最多关注的完全观察的静态数据的相对简单的情况之外，在本文中，我们将回顾贝叶斯网络如何对动态数据和具有不完整观察的数据进行建模。此类数据是研究前沿和实际应用中的常态，贝叶斯网络因其可解释性和可解释性而具有独特的优势，可以对它们进行建模。