PROJECT TITLE :

Bayesian $M$ -Ary Hypothesis Testing: The Meta-Converse and Verdú-Han Bounds Are Tight

ABSTRACT:

Two various precise characterizations of the minimum error likelihood of Bayesian -ary hypothesis testing are derived. The first expression corresponds to the error likelihood of an induced binary hypothesis check and implies the tightness of the meta-converse sure by Polyanskiy et al.; the second expression could be a perform of an information-spectrum measure and implies the tightness of a generalized Verdú-Han lower sure. The formulas characterize the minimum error likelihood of several issues in info theory and facilitate to spot the steps where existing converse bounds are loose.