Competing Fusion for Bayesian Applications

Michael Abramovici, Manuel Neubach, Madjid Fathi, Alexander Holland.

In this paper we address and discuss the problem of learning graphical models like Bayesian networks using structure learning algorithms. We present a new parameterized structure learning approach. A competing fusion mechanism to aggregate expert knowledge stored in distributed knowledge bases or probability distributions is also described. Experimental results of a medical case study show that our approach can improve the quality of the learned graphical model.

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