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Research article, 2003

On the Optimal Weighting of High-Dimensional Bayesian Networks

Pavlenko Tatjana, von Rosen Dietrich


For an augmented Bayesian network classifier we propose a method of scoring a set of feature nodes for the separation strength, wherein we have combined a weighting technique and growing dimension asymptotics in a single framework. We show that the distribution of the weighted classifier is asymptotically Gaussian and establish the weight-function which is optimal in a sense of minimum misclassification probability


Bayesian network; augmenting; separation strength; growing dimension asymptotic; weighted classifier; limiting error probability

Published in

Research report (Centre of Biostochastics)
2003, Volume: 2003, number: 6, pages: 1-18
Publisher: SLU

    SLU Authors

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