广义熵损失函数下维纳过程的贝叶斯估计  

Bayesian Estimation of Wiener Process Based on Generalized Entropy Loss Function

作　　者：欧建军 张安[1] 鄢伟安[3] OU Jian-jun;ZHANG An;YAN Wei-an(Northwestern Polytechnical University,Xi’an 710072,China;Air Force Engineering University,Xi'an 710038,China;East,China Jiaotong University,Nanchang 330013,China)

机构地区：[1]西北工业大学,西安710072 [2]空军工程大学,西安710038 [3]华东交通大学,南昌330013

出　　处：《电光与控制》2019年第8期24-27,共4页Electronics Optics & Control

基　　金：国家自然科学基金(71861011,61703326)

摘　　要：基于广义熵损失函数,分别在无信息先验及共轭先验分布下,获得维纳过程参数及可靠性指标的贝叶斯估计,并将其与极大似然估计、平方损失函数下的贝叶斯估计进行对比讨论。仿真结果表明,广义熵损失函数下的贝叶斯估计均方误差最小,精度最高,同时该估计的表达式比较灵活,能够有效刻画过高估计和过低估计造成风险不同的情形。The Bayesian estimation of the parameters and reliability function for Wiener process are obtained based on the generalized entropy loss function by using both non-informative and conjugate prior distribution and it is compared with the Bayesian estimation under the square loss function and maximum likelihood estimation.The simulation results show that: The Bayesian estimation under the generalized entropy loss function has the smallest mean square error and the highest precision and the expression of the estimation is flexible which can effectively describe the situation where the risks are different due to over- estimation and under-estimation.

关 键 词：可靠性分析 维纳过程 广义熵损失函数 贝叶斯估计 性能退化 

分 类 号：E917[军事]