针对传统假设中个体寿命独立同分布的不足，构建了贝叶斯Weibull共享异质性模型，提出了对寿命服从Weibull分布的产品，运用基于Gibbs抽样的马尔可夫链蒙特卡罗（Markov chain Monte Carlo，MCMC）方法动态模拟出参数后验分布的马尔可夫链，在异质性因子的先验分布为Gamma分布时，给出随机截尾条件下，参数在Weibull共享异质性模型中的贝叶斯估计，提高了计算的精度。借助数据仿真说明了利用WinBUGS（Bayesian inference using Gibbs sampling）软件包进行建模分析的过程，证明了该模型在可靠性应用中的直观性与有效性。

Aimed at the fault of the traditional hypothesis that the lifetimes of the individuals obey the independent identically distribution, this paper constructs the Bayesian Weibull shared frailty model. As for the products whose lifetime distribution belongs to Weibull distribution, this paper brings forward the MCMC method based on Gibbs sampling to simulate dynamically the Markov chain of the parameters' posterior distribution. It also gives out the parameters´ Bayesian estimation of the Weibull shared frailty model in the condition of the random truncated test and the prior distribution of the frailty belonging to the Gamma distribution. The precision of the numeration can thus be improved. Also, this paper utilizes the data's simulation to show the process of setting the model by using the WinBUGS package, and proves the objectivity and validity of the model.