考虑先验信息可信度的后验加权Bayes估计

 先验信息失真及先验样本数量过大会扭曲小子样条件下Bayes融合评估的效果，引入可信度可以改善这个问题。现有的可信度度量方法大都直接基于数据层，即通过判断两种样本是否属于同一分布，这种度量在小子样情况下不太可信，为此提出了一种基于数据物理来源的可信度度量方法。同时归纳了可信度融合评估的准则，分析了现有可信度融合评估方法的不足，并结合正态分布的参数估计问题，给出了一种考虑先验信息可信度的后验加权Bayes估计方法。理论分析和算例都证实了该方法优于常用的Bayes估计方法。

A Novel Posterior-weighted Bayesian Estimation Method Considering the Credibility of the Prior Information

The distortion of prior information and too many prior samples would distort the evaluation results for small sample, which can be improved by introducing the credibility of the prior information. However, most of the measures for the credibility of the prior information are based on data by judging whether the prior samples and the test samples are subjected to the same distribution, which is unreliable when the samples is very few. A measure of credibility from the physics resource of the data is proposed...