We propose and examine statistical test-strategies that are somewhat between the maximum likelihood ratio and Bayes factor methods that are well addressed in the literature. The paper shows an optimality of the proposed tests of hypothesis. We demonstrate that our approach can be easily applied to practical studies, because execution of the tests does not require deriving of asymptotical analytical solutions regarding the type I error. However, when the proposed method is utilized, the classical significance level of tests can be controlled.

中文翻译：

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最优假设检验：从半贝叶斯因子到完全贝叶斯因子

我们提出并检查了介于最大似然比和贝叶斯因子方法之间的统计测试策略，这些方法在文献中得到了很好的解决。该论文显示了所提议的假设检验的最优性。我们证明我们的方法可以很容易地应用于实际研究，因为测试的执行不需要推导出关于 I 类错误的渐近分析解决方案。然而，当使用所提出的方法时，可以控制检验的经典显着性水平。