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The Sample Complexity of Simple Binary Hypothesis Testing
March 26, 2024, 4:49 a.m. | Ankit Pensia, Varun Jog, Po-Ling Loh
stat.ML updates on arXiv.org arxiv.org
Abstract: The sample complexity of simple binary hypothesis testing is the smallest number of i.i.d. samples required to distinguish between two distributions $p$ and $q$ in either: (i) the prior-free setting, with type-I error at most $\alpha$ and type-II error at most $\beta$; or (ii) the Bayesian setting, with Bayes error at most $\delta$ and prior distribution $(\alpha, 1-\alpha)$. This problem has only been studied when $\alpha = \beta$ (prior-free) or $\alpha = 1/2$ (Bayesian), and …
abstract alpha arxiv bayesian beta binary complexity cs.it error free hypothesis math.it math.st prior sample samples simple stat.ml stat.th testing type
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