Compatible and incompatible abstractions in Bayesian networks

Date

2014-05-01

Author

Yet, Barbaros

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The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing decision support models from a combination of domain knowledge and data. The domain knowledge of experts is used to determine the graphical structure of the BN, corresponding to the relationships and between variables, and data is used for learning the strength of these relationships. However, the available data seldom match the variables in the structure that is elicited from experts, whose models may be quite detailed; consequently, the structure needs to be abstracted to match the data. Up to now, this abstraction has been informal, loosening the link between the final model and the experts' knowledge. In this paper, we propose a method for abstracting the BN structure by using four 'abstraction' operations: node removal, node merging, state-space collapsing and edge removal. Some of these steps introduce approximations, which can be identified from changes in the set of conditional independence (CI) assertions of a network.

Subject Keywords

Bayesian networks, Knowledge engineering, Abstraction, Knowledge-based models, Graphical probabilistic models

URI

https://hdl.handle.net/11511/57758

Journal

KNOWLEDGE-BASED SYSTEMS

DOI

https://doi.org/10.1016/j.knosys.2014.02.020

Collections

Graduate School of Informatics, Article

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Citation Formats

B. Yet, “Compatible and incompatible abstractions in Bayesian networks,” KNOWLEDGE-BASED SYSTEMS, pp. 84–97, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57758.