New bounds derived for Active Simple Hypothesis Testing

This paper introduces new upper and lower bounds for the optimal error exponent in Active Simple Hypothesis Testing (ASHT). The research combines a recent Partial Differential Equation (PDE) characterization of the optimal exponent with the comparison principle of viscosity solutions for Hamilton-Jacobi PDEs. The resulting bounds are flexible, parameterized by convex functions and sets, and generalize many existing results in the field. AI

IMPACT This research advances theoretical understanding in statistical hypothesis testing, potentially impacting algorithms in machine learning that rely on efficient data analysis and decision-making.

RANK_REASON The item is an academic paper published on arXiv detailing new theoretical bounds in a specific statistical problem. [lever_c_demoted from research: ic=1 ai=0.7]

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• Active Simple Hypothesis Testing
• arXiv
• Bandits
• Hamilton-Jacobi PDEs
• information theory
• operations research
• partial differential equation
• Vijayan

• paper
• other

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