论文标题
从$ p $ - 价值到假设的后验概率
From $p$-Values to Posterior Probabilities of Hypothesis
论文作者
论文摘要
如Pericchi等人所述，最小贝叶斯因子通常用于将零假设后概率的双向P值转化为下限。 （2017）。在本文中，我们通过转换Vovk（1993）和Sellke等人提出的常用-EPLOG（P）来显示假设的后验概率。 （2001）。这是在使用信息将其近似于确切的贝叶斯因子的最小贝叶斯因子进行调整之后，不仅是当P是p值时，而且还将p是Casella和Berger（2001）的伪p值时。此外，我们将拟合到线性模型的精制版本显示。
Minimum Bayes factors are commonly used to transform two-sided p-values to lower bounds on the posterior probability of the null hypothesis, as in Pericchi et al. (2017). In this article, we show posterior probabilities of hypothesis by transforming the commonly used -eplog(p), proposed by Vovk (1993) and Sellke et al. (2001). This is achieved after adjusting this minimum Bayes factor with the information to approximate it to an exact Bayes factor, not only when p is a p-value but also when p is a pseudo p-value in the sense of Casella and Berger (2001). Additionally, we show the fit to a refined version to linear models.
