Finite-Sample Expansions for the Optimal Error Probability in Asymmetric Binary Hypothesis Testing

The authors derive new sharp bounds and accurate nonasymptotic expansions with explicit constants for the best achievable error probability in asymmetric binary hypothesis testing based on independent and identically distributed observations.