In Bayesian decision theory, the performance of an action is measured by its posterior expected loss. In some cases it may be convenient/necessary to use a non-optimal decision instead of the optimal one. In these cases it is important to quantify the additional loss we incur and evaluate whether to use the non-optimal decision or not. In this article we study the predictive probability distribution of a relative measure of the additional loss and its use to define sample size determination criteria in a general testing set-up.

中文翻译：

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假设检验中非最佳动作的预测性能

在贝叶斯决策理论中，一项行动的绩效是通过其后继预期损失来衡量的。在某些情况下，使用非最佳决策代替最佳决策可能很方便/有必要。在这些情况下，量化我们造成的额外损失并评估是否使用非最佳决策非常重要。在本文中，我们研究了附加损失的相对度量的预测概率分布及其在常规测试设置中用于定义样本量确定标准的用途。