This paper is concerned with the construction of prior free posterior distributions which rely on the use of one step ahead predictive distribution functions. These are typically more straightforward to motivate than prior distributions. Recent interest has been with Hill's $A_n$ prediction model through what has become known as conformal prediction. This model predicts the next observation to lie with equal probability in the intervals created by the observed data. The prediction model generates complete data sets which can be used to provide posterior inference on any statistic of interest.

翻译：本文关注的是基于一步预测分布函数构建无先验的后验分布，这类方法比先验分布更易于论证其合理性。近期研究兴趣集中于通过"共形预测"（conformal prediction）所熟知的Hill $A_n$预测模型。该模型预测下一个观测值以等概率落入由已有观测数据划分的区间内。此预测模型能生成完整数据集，并可用于对任意感兴趣统计量进行后验推断。