We consider a decision problem in which data are unordered (unlabeled). Recent studies of this problem provide a complete asymptotic characterization of the decision performance for large data size, which is the solution of a convex optimization problem. While this is fully satisfactory from a numerical viewpoint, limited insight is offered because a closed-form explicit expression for the decision performance is, in general, not available. For binary observations and the challenging regime of low-detectability, we derive an extremely simple analytical solution, investigate its properties and discuss the obtained physical insights.
Shuffled bits in the low-detectability regime
Marano S.;
2019
Abstract
We consider a decision problem in which data are unordered (unlabeled). Recent studies of this problem provide a complete asymptotic characterization of the decision performance for large data size, which is the solution of a convex optimization problem. While this is fully satisfactory from a numerical viewpoint, limited insight is offered because a closed-form explicit expression for the decision performance is, in general, not available. For binary observations and the challenging regime of low-detectability, we derive an extremely simple analytical solution, investigate its properties and discuss the obtained physical insights.File in questo prodotto:
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