摘要
针对动态贝叶斯网络(DBN)结构学习中涉及的度量分解问题,提出了DBN度量分解后的相关性能。首先,细化了DBN的贝叶斯信息度量(BIC)及贝叶斯-狄里克莱(BD)度量公式,通过表达式的分析,讨论了分解后的相关性质,进而提出了由分解公式提供给DBN结构学习的相关性能。其次,通过设计的性能分析仿真实验,验证了提出的若干设想,即将BN结构学习算法移植到DBN结构学习的可行性及分解降低算法复杂度等问题,并提出了寻找DBN快速结构学习算法的有效思路。
Some correlative properties on dynamic Bayesian networks（DBN） structure metric decomposition for DBN structure learning are proposed. Firstly, DBN＇s Bayesian information matric（BIC） and Bayesian-Dirichlet metric （BD） decomposition formula are further divided into two parts. Some characters are discussed based on the decomposition formula, and more useful properties are developed. Secondly, a simulation model is designed to verified properties. The properties include two problems, one is the transplantation problem that many static state Bayesian networks（BN） structure learning algorithm can be used to DBN structure learning, the other is computation complexity problem that DBN structure learning time can be lower through DBN structure decomposition. In the end, a good idea is presented for finding a faster and efficient DBN structure learning algorithm.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第4期938-946,共9页
Systems Engineering and Electronics
基金
国家自然科学基金(90205019)
中国博士后科学基金(2008043410)
陕西省教育厅专项科研基金(07JK277)
“十一五”兵器预研支撑基金(62301110115,62301110408)资助课题
关键词
DBN
结构学习
度量分解
移植性
计算复杂度
dynamic Bayesian networks
structure learning
metric decomposition
transplantation
computation complexity
