Modifications of Uncertain Data: A Bayesian Framework for Belief Revision

The inherent uncertainty pervasive over the real world often forces business decisions to be made using uncertain data. The conventional relational model does not have the ability to handle uncertain data. In recent years, several approaches have been proposed in the literature for representing uncertain data by extending the relational model, primarily using probability theory. The aspect of database modification, however, has not been addressed in prior research. It is clear that any modification of existing probabilistic data, based on new information, amounts to the revision of one's belief about real-world objects. In this paper, we examine the aspect of belief revision and develop a generalized algorithm that can be used for the modification of existing data in a probabilistic relational database. The belief revision scheme is shown to be closed, consistent, and complete.

• 저널Information Systems Research · 11(1) · 1–16
• 토픽Bayesian Modeling and Causal Inference · Artificial Intelligence
• DOI10.1287/isre.11.1.1.11785
• 저자Debabrata Dey, Sumit Sarkar