Bayesian network models for incomplete and dynamic data. (English) Zbl 1541.62070
Summary: 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.
{© 2020 The Authors. Statistica Neerlandica © 2020 VVS.}
{© 2020 The Authors. Statistica Neerlandica © 2020 VVS.}
MSC:
| 62F15 | Bayesian inference |
| 68T35 | Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence |
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