Approximate representation of probabilistic data in expert systems |
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| Affiliation: | 1. Department of Information Systems and Decision Sciences, College of Business Administration, Louisiana State University, 3190 CBA, Baton Rouge, LA 70803, USA;2. Graduate School of Business, Stanford University, Stanford, CA, USA;3. W.E. Simon Graduate School of Business Administration, University of Rochester, Rochester, NY, USA |
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| Abstract: | We propose the use of a third-order approximation for the representation of probabilistic data in expert systems and compare it to tree-structured representations. The differences are illustrated using the example of a reliability problem. We show that using the third-order representation results in significantly reduced losses as compared to tree structures, with a small increase in computational complexity. We present heuristic and exact techniques to determine the optimal third-order representation and propose a decomposition technique that allows the exact algorithm to be efficiently used for solving large problem instances. |
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