Contributors:Vazquez-Vilar, G, Tauste Campo, A, Fabregas, AG, Martinez, A
Two alternative exact characterizations of the minimum error probability of Bayesian M-ary hypothesis testing are derived. The first expression corresponds to the error probability of an induced binary hypothesis test and implies the tightness of the meta-converse bound by Polyanskiy et al.; the second expression is a function of an information-spectrum measure and implies the tightness of a generalized Verdú-Han lower bound. The formulas characterize the minimum error probability of several problems in information theory and help to identify the steps where existing converse bounds are loose.
Contributors:Scarlett, J, Somekh-Baruch, A, Martinez, A, Guillén I Fàbregas, A