Bayesian Network Models for Incomplete and Dynamic Data
This work is incremental, as it reviews existing methods for applying Bayesian networks to common data issues in research and applications.
The paper addresses the challenge of modeling incomplete and dynamic data using Bayesian networks, highlighting their capability to handle such complex data types effectively.
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.