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Structure Learning of Bayesian Networks Using Dual Genetic Algorithm
IEICE - Transactions on Information and Systems
Paper rating vs. paper ranking
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DC proposal: decision support methods in community-driven knowledge curation platforms
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part II
Combining uncertainty and imprecision in models of medical diagnosis
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Automated interviews on clinical case reports to elicit directed acyclic graphs
Artificial Intelligence in Medicine
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We present an objective approach for evaluating probability and structure elicitation methods in probabilistic models. The main idea is to use the model derived from the experts' experience rather than the true model as the standard to compare the elicited model. We describe a general procedure by which it is possible to capture the data corresponding to the expert's beliefs, and we present a simple experiment in which we utilize this technique to compare three methods for eliciting discrete probabilities: 1) direct numerical assessment, 2) the probability wheel, and 3) the scaled probability bar. We show that for our domain, the scaled probability bar is the most effective tool for probability elicitation