Bayesian network models for incomplete and dynamic data. (7th January 2020)

• Record Type: Journal Article
• Authors: Scutari, Marco
• Other Names: Vinciotti Veronica guestEditor.; Wit Ernst C. guestEditor.
• Is Part Of: Statistica Neerlandica. Volume 74:Number 3(2020)
• Page Start: 397
• Page End: 419
• Publication Date: 2020-01-07
• Subjects: Bayesian networks -- dynamic data -- incomplete data -- inference -- structure learning; Statistics -- Periodicals; 519.5; 314.92
• DOI: 10.1111/stan.12197
• Languages: English
• ISSNs: 0039-0402

Abstract

Abstract : 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.