IIRCT下泊松分布参数多变点模型的贝叶斯估计

通过添加缺损的寿命变量数据得到了带有不完全信息随机截尾试验下泊松分布参数多变点模型的完全数据似然函数,研究了变点位置参数和其它参数的满条件分布.利用Gibbs抽样与Metropolis-Hastings算法相结合的MCMC方法对各参数的满条件分布分别进行了抽样,把Gibbs样本的均值作为各参数的贝叶斯估计,并且详细介绍了MCMC方法的实施步骤.最后进行了随机模拟试验,试验结果表明各参数贝叶斯估计的精度都较高.

Bayesian Estimation of Parameter of Poisson Distribution with Multiple Change Points for Random Censoring Test Model with Incomplete Information

By filling in the missing data of the life variable,the complete data likelihood function of Poisson distribution with multiple change points for IIRCT is obtained.The full conditional distributions of change-point positions and other parameters are studied.Every parameter is sampled from its full conditional distribution respectively,using MCMC method of Gibbs sampling together with Metropolis-Hastings algorithm,and the means of Gibbs samples are taken as Bayesian estimations of the parameters.The implementation steps of MCMC method are introduced in detail.The random simulation tests are conducted,and the results show that Bayesian estimations of the parameters are fairly accurate.