2022/08/28 by Akira Kamatsuka, Kamatsuka, Akira
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning and Algorithms #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2208.13152
openalex publication_date 2022/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider a problem of simple hypothesis testing using a randomized test via a tunable loss function proposed by Liao et al. In this problem, we derive results that correspond to the Neyman--Pearson lemma, the Chernoff--Stein lemma, and the Chernoff-information in the classical hypothesis testing problem. Specifically, we prove that the optimal error exponent of our problem in the Neyman--Pearson's setting is consistent with the classical result. Moreover, we provide lower bounds of the optimal Bayesian error exponent.