Advances in Bayesian Networks
A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".
Deadline for manuscript submissions: closed (28 March 2024) | Viewed by 349
Special Issue Editors
Interests: Bayesian networks: theoretical and practical implications
Special Issue Information
Dear Colleagues,
This Special Issue on Advances in Bayesian Networks is focused on the most relevant developments in the field of probabilistic expert systems and, in particular, in Bayesian networks (BNs). BNs are increasingly popular as models for handling complex and modular problems thanks to their ability to simply handle uncertainty. Furthermore, BNs, due to their inferential machine permitting scenarios building, are a powerful decision support tool supporting decision processes. Up to now, the scientific community has still been handling open issues about BNs, mainly linked to their learning from data, their use for casual reasoning, and their potential role as an inferential machine learning tool. Contributions related to the model learning from data or to still unexplored applications are welcome in this Special Issue. The purpose of this Special Issue is thus to combine the recent contributions of the community of BNs modelers with the aim of supporting the literature in these open issues.
This Special Issue invites high-quality contributions related to, for example, but not exclusively, the following:
- Applications in healthcare field;
- Applications in higher education field;
- Applications in financial education field;
- Applications in bank sector;
- Applications to complex managerial problems and business issues;
- BNs for measurement errors detection;
- BNs for merging different data sources;
- BNs and casual reasoning;
- Object-oriented BNs for accompanying composite indicators;
- Structural learning algorithms for building BNs from data.
Dr. Flaminia Musella
Prof. Dr. Paola Vicard
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- learning Bayesian networks
- casual reasoning and Bayesian networks
- supporting decision process
- inferential machine learning
- small-big data integration
- object-oriented Bayesian networks
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