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FEGC
2008

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Structure Inference of Bayesian Networks from Data: A New Approach Based on Generalized Conditional Entropy

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Structure Inference of Bayesian Networks from Data: A New Approach Based on Generalized Conditional Entropy

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We propose a novel algorithm for extracting the structure of a Bayesian network from a dataset. Our approach is based on generalized conditional entropies, a parametric family of entropies that extends the usual Shannon conditional entropy. Our results indicate that with an appropriate choice of a generalized conditional entropy we obtain Bayesian networks that have superior scores compared to similar structures obtained by classical inference methods.

Dan A. Simovici, Saaid Baraty

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Added	29 Oct 2010
Updated	29 Oct 2010
Type	Conference
Year	2008
Where	FEGC
Authors	Dan A. Simovici, Saaid Baraty

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