Abstract
Sequential approaches to automated test selection for diagnostic Bayesian networks include a stopping criterion for deciding in each iteration whether or not gathering of further evidence is opportune. We study the computational complexity of the problem of deciding when to stop evidence gathering in general and show that it is complete for the complexity class NP PP; we show that the problem remains NP-complete even when it is restricted to networks of bounded treewidth. We will argue however, that by reasonable further restrictions the problem can be feasibly solved for many realistic applications.
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van der Gaag, L.C., Bodlaender, H.L. (2011). On Stopping Evidence Gathering for Diagnostic Bayesian Networks. In: Liu, W. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2011. Lecture Notes in Computer Science(), vol 6717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22152-1_15
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DOI: https://doi.org/10.1007/978-3-642-22152-1_15
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