摘要
学习具有丢失数据的贝叶斯网络结构主要采用结合EM算法的打分—搜索方法,其效率和可靠性比较低。针对此问题建立一个新的具有丢失数据的贝叶斯网络结构学习算法。该方法首先用Kullback-Leibler(KL)散度来表示同一结点的各个案例之间的相似程度,然后根据Gibbs取样来得出丢失数据的取值。最后,用启发式搜索完成贝叶斯网络结构的学习。该方法能够有效避免标准Gibbs取样的指数复杂性问题和现有学习方法存在的主要问题。
At present, the algorithm of learning Bayesian structure with missing data is mainly based on the search and scoring method combined with EM algorithm. The algorithm had low efficiency. A new algorithm of learning Bayesian network structure with missing data is presented. KL divergence is used to express the similarity between the cases. Then the value of the missing data is draw according to the Gibbs sampling. Finally, heuristic search is used to complete the learning of Bayesian network structure, This method can avoid the exponential complexity of standard Gibbs sampling and the main problems in the existing algorithm.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第1期142-143,250,共3页
Computer Engineering and Design
基金
国家自然科学基金项目(60472017、30670699)
关键词
KL散度
Gibbs取样
结构学习
丢失数据
贝叶斯网络
Kullback-Leibler divergence
Gibbs sampling
structure learning
missing data
Bayesian network
