Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 Verdu-Han lor 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.
['Gonzalo Vazquez-Vilar', 'Adria Tauste Campo', 'Albert Guillen i Fabregas', 'Alfonso Martinez']
I am writing a research paper on a idea Bayesian $M$ -Ary Hypothesis Testing: The Meta-Converse and Verdú-Han Bounds Are Tight
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian $M$ -Ary Hypothesis Testing: The Meta-Converse and Verd\u00fa-Han Bounds Are Tight"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 Verdu-Han lor 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."}]