Issues in Bayesian analysis of neural network models
Neural Computation
Knowledge-based neurocomputing
Knowledge-based neurocomputing
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Bayesian Networks in Ovarian Cancer Diagnosis: Potentials and Limitations
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
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We describe a methodology based on a dual Belief Network-Multilayer Perceptron representation to build Bayesian classifiers. This methodology combines efficiently the prior domain knowledge and statistical data. We overview how this Bayesian methodology is able (1) to define constructively a valuable "informative" prior for black-box models, (2) to provide uncertainty information with predictions and (3) to handle missing values based on an auxiliary domain model. We assume that the prior domain model is formalized as a Belief Network (since this representation is a practical solution to acquiring prior domain knowledge) while we use black-box models (such as Multilayer Perceptrons) for learning to utilize the statistical data. In a medical task of predicting the malignancy of ovarian masses we demonstrate these two symbiotic applications of Belief Network models and summarize the practical advantages of the Bayesian approach.