Learning the BN Structure Based on the Sum of Mutual Information

The Crossing Entropy is defined to scale the similar level of two probability distribution. In many papers on learning BN structure,the Crossing Entropy was used as an indicator of measuring the learning accuracy of an algorithm.The known scoring metrics for learning BN structure is analyzed in this paper,then a new scoring metrics Sum of Mutual Information is proposed based on the information theory.At last,two algorithm for learning BN structure by SIM is represented.

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Clc Number:

TP181