为研究串联系统下多部件应力-强度模型的可靠性问题,基于Kumaraswamy分布,采用极大似然法给出参数及应力-强度模型可靠度的极大似然估计(maximum likelihood estimation,MLE);再利用Jeffreys准则构造无信息先验分布,运用马尔可夫链蒙特...为研究串联系统下多部件应力-强度模型的可靠性问题,基于Kumaraswamy分布,采用极大似然法给出参数及应力-强度模型可靠度的极大似然估计(maximum likelihood estimation,MLE);再利用Jeffreys准则构造无信息先验分布,运用马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)方法给出参数及应力-强度模型可靠度的贝叶斯估计;最后,利用逆矩估计方法给出参数及应力-强度模型可靠度的逆矩估计(inverse moment estimation,IME)。数值模拟结果表明,在不同系统可靠度及不同样本量条件下,通过对3种估计方法的数值进行比较发现贝叶斯估计效果最好,IME优于MLE。该研究为探讨串联系统多部件应力-强度模型可靠性提供了一定的理论基础。展开更多
In this paper we propose an experimental method to choose a prior distribution. Different from many re-searchers, who offered lots of principles that separated from sample information, we consider it a Bayesian discri...In this paper we propose an experimental method to choose a prior distribution. Different from many re-searchers, who offered lots of principles that separated from sample information, we consider it a Bayesian discrimina-tion problem combining with the sample information. We introduce the concept of Posterior belief about prior distri-butions. With the well-known Bayes theorem we give out a formula to calculate it and propose a method to discrirni-nate a prior between prior distributions-- Highest Posterior Belief (HPB). We also show that under certain condition,the HPB method is identical with the ML-I method.展开更多
文摘为研究串联系统下多部件应力-强度模型的可靠性问题,基于Kumaraswamy分布,采用极大似然法给出参数及应力-强度模型可靠度的极大似然估计(maximum likelihood estimation,MLE);再利用Jeffreys准则构造无信息先验分布,运用马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)方法给出参数及应力-强度模型可靠度的贝叶斯估计;最后,利用逆矩估计方法给出参数及应力-强度模型可靠度的逆矩估计(inverse moment estimation,IME)。数值模拟结果表明,在不同系统可靠度及不同样本量条件下,通过对3种估计方法的数值进行比较发现贝叶斯估计效果最好,IME优于MLE。该研究为探讨串联系统多部件应力-强度模型可靠性提供了一定的理论基础。
文摘In this paper we propose an experimental method to choose a prior distribution. Different from many re-searchers, who offered lots of principles that separated from sample information, we consider it a Bayesian discrimina-tion problem combining with the sample information. We introduce the concept of Posterior belief about prior distri-butions. With the well-known Bayes theorem we give out a formula to calculate it and propose a method to discrirni-nate a prior between prior distributions-- Highest Posterior Belief (HPB). We also show that under certain condition,the HPB method is identical with the ML-I method.
