2013/02/27 by Runping Qi, Qi, Runping, Nevin L. Zhang +3
Computer Science · Decision Sciences · #Bayesian Modeling and Causal Inference #AI-based Problem Solving and Planning #Data Quality and Management
paper · pdf · doi:10.48550/arxiv.1302.6840
While influence diagrams have many advantages as a representation framework for Bayesian decision problems, they have a serious drawback in handling asymmetric decision problems. To be represented in an influence diagram, an asymmetric decision problem must be symmetrized. A considerable amount of unnecessary computation may be involved when a symmetrized influence diagram is evaluated by conventional algorithms. In this paper we present an approach for avoiding such unnecessary computation in influence diagram evaluation.