论文标题
新先验的后期适应
Posterior Adaptation With New Priors
论文作者
论文摘要
如果原始类先验开始改变，基于直接估计和后验概率的直接估计和分析将降低分类方法。我们证明，可以从其原始类后代和数据集先验的测试示例中恢复数据可能性的唯一解决方案。鉴于恢复的可能性和一组新的先验，可以使用贝叶斯规则重新计算后代，以反映新先验的影响。该方法易于计算，并允许对原始后代的动态更新。
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.
