Admissible decision rules are strategies used in statistical decision theory that are never worse than any other decision rule for all possible states of nature. They help identify the most efficient approaches by ensuring that any selected rule has an acceptable level of performance compared to alternatives. The concept connects closely with evaluating risks and the Bayes risk, which measures the expected loss associated with a decision under uncertainty.
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