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.

翻译：在Pericchi等人案(2017年)中,最小贝亚系数通常用于将双面的p值转换为无效假设的事后概率的下限,在本条中,我们通过改变Vovk(1993年)和Sellke等人(2001年)提出的常用的-eplog(p)来显示假设的后端概率。 这是在调整这一最小贝亚系数之后,根据信息将其与精确的Bayes系数相近,不仅在p为p值时,而且在p是Casella和Berger(2001年)意义上的假冒的p值时。 此外,我们显示精细版适合线性模型。