摘要
在使用Bayes方法对系统进行多源先验数据的性能评估时,综合多源先验数据,利用得到的融合先验分布与实验数据得到后验分布,通过对后验分布进行参数估计得到性能评估结果。提出了一种基于Kullback-Leibler散度的多源先验数据加权融合方法,能够有效整合多源先验数据。使用常用的马尔可夫链蒙特卡罗方法对Bayes后验分布进行参数估计,对比了不同建议分布对抽样结果的影响,提出了一种适用于低维建议分布的自适应构造方法,能够有效选取合适的建议分布函数,提升抽样效率。
When using the Bayes method to evaluate the performance of the system with multi-source prior data,the multi-source prior data is fused,the posterior distribution is calculated by synthesizing the fused prior distribution and test data.The parameters of posterior distribution are estimated to obtain the performance evaluation results.A weighted fusion method of multi-source prior data based on Kullback-Leibler divergence is proposed,which can effectively integrate the multi-source prior data.The commonly used Markov Chain Monte Carlo method is used to estimate the parameters of Bayes posterior distribution.The influence of different proposal distributions on the sampling results is compared,and an adaptive construction method for low-dimensional proposal distributions is proposed,which can effectively select a proper proposal distribution and improve the efficiency of sampling.
作者
刘灏哲
李伟
马萍
杨明
Liu Haozhe;Li Wei;Ma Ping;Yang Ming(Control and Simulation Center,Harbin Institute of Technology,Harbin 150001,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2021年第11期2673-2680,共8页
Journal of System Simulation
