This paper mainly deals with the Bayesian statistical inference theory on the VAR(p) forecasting model based on the parameters’ Minnesota conjugate prior distribution,including the prior distribution’s structure, th...This paper mainly deals with the Bayesian statistical inference theory on the VAR(p) forecasting model based on the parameters’ Minnesota conjugate prior distribution,including the prior distribution’s structure, the parameters’ posterior distribution, and compares the forecasting accuracy of AR,VAR and BVAR model.展开更多
为了提高剩余寿命预测的可信度,针对进行过加速老化试验的产品,提出利用Gamma过程参数的非共轭先验分布进行Bayesian统计推断的剩余寿命预测方法.将加速老化数据作为先验信息,利用Gamma过程进行老化建模,通过加速因子获得形状参数在工...为了提高剩余寿命预测的可信度,针对进行过加速老化试验的产品,提出利用Gamma过程参数的非共轭先验分布进行Bayesian统计推断的剩余寿命预测方法.将加速老化数据作为先验信息,利用Gamma过程进行老化建模,通过加速因子获得形状参数在工作应力下的折算值,使用Anderson-Darling统计量确定随机参数的先验分布.将产品工作中的少量实测数据作为现场信息,利用基于Gibbs抽样的Markov Chain Monte Carlo(MCMC)仿真得到参数的后验均值.以某型导弹电连接器为例说明了该方法的研究意义和工程应用价值.展开更多
文摘This paper mainly deals with the Bayesian statistical inference theory on the VAR(p) forecasting model based on the parameters’ Minnesota conjugate prior distribution,including the prior distribution’s structure, the parameters’ posterior distribution, and compares the forecasting accuracy of AR,VAR and BVAR model.
文摘为了提高剩余寿命预测的可信度,针对进行过加速老化试验的产品,提出利用Gamma过程参数的非共轭先验分布进行Bayesian统计推断的剩余寿命预测方法.将加速老化数据作为先验信息,利用Gamma过程进行老化建模,通过加速因子获得形状参数在工作应力下的折算值,使用Anderson-Darling统计量确定随机参数的先验分布.将产品工作中的少量实测数据作为现场信息,利用基于Gibbs抽样的Markov Chain Monte Carlo(MCMC)仿真得到参数的后验均值.以某型导弹电连接器为例说明了该方法的研究意义和工程应用价值.
