Upper and lower bounds on the minimax asymptotic error probability for adversarial hypothesis testing with an S-state finite-state-machine tester match in their exponential dependence on S for a class of problems.
Minimax tests and the Neyman-Pearson lemma for capacities
2 Pith papers cite this work. Polarity classification is still indexing.
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Optimal joint detection and estimation under distributional uncertainty is achieved by maximizing an f-similarity to identify least favorable distributions in both Bayesian and Neyman-Pearson settings.
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Memory Constrained Adversarial Hypothesis Testing
Upper and lower bounds on the minimax asymptotic error probability for adversarial hypothesis testing with an S-state finite-state-machine tester match in their exponential dependence on S for a class of problems.
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Minimax Optimal Procedures for Joint Detection and Estimation
Optimal joint detection and estimation under distributional uncertainty is achieved by maximizing an f-similarity to identify least favorable distributions in both Bayesian and Neyman-Pearson settings.