寻找一些分布中的参数的具有预先给定宽度和预先给定覆盖概率的置信区间是令人感兴趣的。
Finding confidence intervals with prescribed width and prescribed coverage probability for some parameters in distributions is of great interest.
当参数限制在某一范围内并服从一致的分布，且多余参数未知时，其贝叶斯置信区间有很高的置信概率。
The Bayesian credible intervals that arise when a parameter is given a uniform distribution over the restricted range and nuisance parameters are unknown have good frequentist coverage probabilities.
针对模式分类中高置信度的先验概率分布难以设定的问题，提出了一种新的应用贝叶斯分析进行模式分类的方法。
To overcome the hardship of enacting the pre-probability distribution with high certainty factor, this paper proposes one novel way of applying Bayes analysis to classify pattern.
应用推荐