Saddlepoint Approximation of the Error Probability of Binary Hypothesis Testing

Gonzalo Vazquez-Vilar, Albert Guillen i Fabregas, Tobias Koch, Alejandro Lancho

Abstract: We propose a saddlepoint approximation of the error probability of a binary hypothesis test between two i.i.d. distributions. The approximation is accurate, simple to compute, and yields a unified analysis in different asymptotic regimes. The proposed formulation is used to efficiently compute the meta-converse lower bound for moderate block-lengths in several cases of interest.