  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.
