Bayesian Network Modelling in Data Sparse Environments
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 15 September 2024 | Viewed by 997
Special Issue Editors
Interests: probabilistic modelling; applied maths; risk analysis; decision making under uncertainty; uncertainty quantification; structured expert judgement; elicitation protocols
Special Issue Information
Dear Colleagues,
Bayesian networks (BNs) are graphical representations (i.e., directed acyclic graphs, DAGs) of the joint probability distribution of dependent variables. The DAG captures (conditional) independencies among variables, which enables a convenient factorization of the joint distribution. BNs have found applications in many diverse domains.
Building BNs consists of two main steps: (1) structure specification and (2) domain-specific parameterization. However, these steps are iterative when communicated to stakeholders, monitored and reviewed. They are frequently refined using domain experts’ input.
Both structure and parameters can be obtained either from data or experts, but they are typically obtained using a combination of both. Despite the current data-rich environment, often there are insufficient data to evaluate potential future events, risks, or opportunities, or to represent their interactions.
While formal protocols exist to quantify parameters in data-sparse environments, there is a gap in well-defined procedures for DAG construction. More research is required to appropriately address the inherent subjectivity involved in constructing BNs in such environments. Moreover, transparency and rigor in reporting, documenting, and justifying all choices made during the BN modeling process should be made a priority.
We invite submissions, including original research articles and reviews, both from an applied perspective as well as methodological developments relating to all issues outlined above.
Dr. Anca Hanea
Dr. Tina Nane
Guest Editors
Manuscript Submission Information
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Keywords
- Bayesian networks
- expert judgement
- elicitation protocols
- dependence modelling
- uncertainty analysis
- data-sparse environments
