We describe Bayes factors functions based on z, t, $\chi^2$, and F statistics and the prior distributions used to define alternative hypotheses. The non-local alternative prior distributions are centered on standardized effects, which index the Bayes factor function. The prior densities include a dispersion parameter that models the variation of effect sizes across replicated experiments. We examine the convergence rates of Bayes factor functions under true null and true alternative hypotheses. Several examples illustrate the application of the Bayes factor functions to replicated experimental designs and compare the conclusions from these analyses to other default Bayes factor methods.

翻译：本文描述了基于z统计量、t统计量、$\chi^2$统计量和F统计量的贝叶斯因子函数，以及用于定义备择假设的先验分布。非局部备择先验分布以标准化效应为中心，该效应作为贝叶斯因子函数的索引。先验密度包含一个离散参数，用于模拟重复实验中效应大小的变异。我们考察了在真实原假设和真实备择假设下贝叶斯因子函数的收敛速度。多个示例展示了贝叶斯因子函数在重复实验设计中的应用，并将这些分析得出的结论与其他默认贝叶斯因子方法进行了比较。