finally, a new kind of methods on how to classify a sample into one of the several known populations in terms of posterior probability ratio established by the sample's predictive density functions when the unknown parameters " prior distributions are diffuse prior and minnesota prior or normal-inverted wishart distribution 最后,利用参数的充分统计量,根据后验概率比构造了一类新的基于扩散先验分布和正态?逆wishart先验分布的多总体贝叶斯分类识别方法。
the class of distributions includes the weibull, burr-type x, pareto and beta distributions . a proper general prior density function is suggested, and predictive density functions are obtained in one-and two-sample cases when the history sample is a type ii double censored sample . illustrative examples are given 在type双删失数据场合下,讨论了双参数burr-type分布参数的贝叶斯估计,在所取的损失函数分别为平方损失,linex损失,熵损失函数下得到了参数的贝叶斯估计,并且给出一种近似算法。