Using the sub-Gaussian norm of the Bernoulli random variable, this paper presents the explicit and informative error lower bounds for binary and multiple hypothesis testing in terms of the KL divergence non-asymptotically. Some numerical comparisons are also demonstrated.

中文翻译：

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假设检验的非渐近亚高斯误差界限

使用伯努利随机变量的亚高斯范数，本文根据 KL 散度非渐近地给出了二元和多假设检验的显式和信息性误差下限。还展示了一些数值比较。