基于互信息和测度学习信度网结构

交叉熵是对一个分布与其近似分布的接近程度的度量。在许多关于信度网结构的学习文献中，都将交叉熵作为检验算法学习效果的一个指标。笔者直接从交叉熵最优这一指标出发，在分析已有测度的基础上，提出了一个新的测度－互信息和测度，并证明了该测度的可分解性质。最后，给出了利用互信息和测度进行信度网结构学习的两种启发式搜索算法。

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.