Classification approaches based on the direct estimation and analysis of posterior probabilities will degrade if the original class priors begin to change. We prove that a unique (up to scale) solution is possible to recover the data likelihoods for a test example from its original class posteriors and dataset priors. Given the recovered likelihoods and a set of new priors, the posteriors can be re-computed using Bayes' Rule to reflect the influence of the new priors. The method is simple to compute and allows a dynamic update of the original posteriors.

翻译：根据直接估计和分析事后概率的分类方法,如果原始类别前科开始改变,则会退化。我们证明,一种独特的(至规模的)解决办法能够从原类后科和数据集前科中恢复测试示例的数据可能性。鉴于回收的可能性和一套新的前科,后科可以使用贝斯规则重新计算,以反映新的前科的影响。这种方法简单计算,并允许对原类后科进行动态更新。