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Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints
Neural Information Processing Systems
In this paper, we derive a method to refine a Bayes network diagnostic model by exploiting constraints implied by expert decisions on test ordering. At each step, the expert executes an evidence gathering test, which suggests the test's relative diagnostic value. We demonstrate that consistency with an expert's test selection leads to non-convex constraints on the model parameters. We incorporate these constraints by augmenting the network with nodes that represent the constraint likelihoods.dblp:conf/nips/KhanPA11 fatcat:3654zq2agnasnpx6uxgehpuoki