A statistical view of uncertainty in expert systems
Artificial intelligence and statistics
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Neural and conceptual interpretation of PDP models
Parallel distributed processing
Neural networks for medical diagnosis
Handbook of neural computer applications
Systems to Support Health Policy Analysis: Theory, Models, and Uses
Systems to Support Health Policy Analysis: Theory, Models, and Uses
Handbook of Neural Computing Applications
Handbook of Neural Computing Applications
Artificial neural networks for knowledge representation: analysis of problem structure based on the bayes' theorem and conditional nonindependence
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The article describes how artificial neural networks with special designs can be applied to approximate a subjective Bayesian decision model without the assumption of conditional independence. New techniques are proposed to resolve some of the practical difficulties during the processes of problem structuring, knowledge elicitation, quantitative modeling, and model interpretation. A Bayesian model considering the conditional dependencies to predict a teenager's marijuana use was constructed by experts using these techniques, and compared to another conventional Bayesian model which assumed conditional independence. The new approach without the assumption of conditional independence had predictive power (r = 0.7) in the test of linearity compared to the conventional approach (r = 0.58) on a data set (n = 129). Its receiver operating characteristic curve dominated the alternative approach within the range (true positive fraction 0.7) that we were interested in. The interpretations of the possible conditional dependencies provided by the artificial neural network after the training process were consistent with the expert's descriptions.