We describe Bayes factors based on z, t, $\chi^2$, and F statistics when non-local moment prior distributions are used to define alternative hypotheses. The non-local alternative prior distributions are centered on standardized effects. The prior densities include a dispersion parameter that can be used to model prior precision and the variation of effect sizes across replicated experiments. We examine the convergence rates of Bayes factors under true null and true alternative hypotheses and show how these Bayes factors can be used to construct Bayes factor functions. An example illustrates the application of resulting Bayes factors to psychological experiments.

翻译：本文描述了当采用非局部矩先验分布定义备择假设时，基于z统计量、t统计量、$\chi^2$统计量与F统计量的贝叶斯因子计算方法。非局部备择先验分布以标准化效应为中心，其密度函数包含一个可用于建模先验精度及重复实验中效应量变异的离散参数。我们考察了在真实零假设与真实备择假设下贝叶斯因子的收敛速率，并展示了如何利用这些贝叶斯因子构建贝叶斯因子函数。通过一个实例说明了所得贝叶斯因子在心理学实验中的应用。