We develop a representation of a decision maker's uncertainty based on e-variables. Like the Bayesian posterior, this *e-posterior* allows for making predictions against arbitrary loss functions that may not be specified ex ante. Unlike the Bayesian posterior, it provides risk bounds that have frequentist validity irrespective of prior adequacy: if the e-collection (which plays a role analogous to the Bayesian prior) is chosen badly, the bounds get loose rather than wrong, making *e-posterior minimax* decision rules safer than Bayesian ones. The resulting *quasi-conditional paradigm* is illustrated by re-interpreting a previous influential partial Bayes-frequentist unification, *Kiefer-Berger-Brown-Wolpert conditional frequentist tests*, in terms of e-posteriors.

翻译：我们根据电子变量来代表决策者的不确定性。 和巴耶斯子孙一样,这个“ e-posteriel”可以预测任意损失功能,但事先可能无法具体说明。 与巴耶斯后辈不同的是,它提供了具有常态有效性的风险界限,而不论先前是否足够:如果电子集合(其作用类似于巴耶斯先辈)选择不当,界限就会松散,而不是错误,使“ e-pose neides minimax*” 规则比巴耶斯后辈规则更安全。 由此产生的“ quasi-stestic union ” 模式* 通过重新解释先前具有影响力的巴耶斯- 复古主义者部分统一, * Kiefer-Berger-Brown-Wolpert 有条件常客测试*, 以电子外观为例。