Using Potential Influence Diagrams for Probabilistic Inference and Decision Making
This work provides theoretical insights for researchers in probabilistic inference and decision analysis, but it appears incremental as it builds on existing diagram representations.
The paper tackles the relationship between potential and conditional influence diagrams, showing how to convert between them and interpret operations, but does not report concrete numerical results.
The potential influence diagram is a generalization of the standard "conditional" influence diagram, a directed network representation for probabilistic inference and decision analysis [Ndilikilikesha, 1991]. It allows efficient inference calculations corresponding exactly to those on undirected graphs. In this paper, we explore the relationship between potential and conditional influence diagrams and provide insight into the properties of the potential influence diagram. In particular, we show how to convert a potential influence diagram into a conditional influence diagram, and how to view the potential influence diagram operations in terms of the conditional influence diagram.