Alexander Shulman
Abstract
A game model is introduced for statistical signal detection, where error costs are too uncertain for a Bayes decision rule. The minimax decision strategy is mixed. It is shown that the optimum decision probabilities are equal to the a posteriori probabilities of the alternatives.
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A game model is introduced for statistical signal detection, where error costs are too uncertain for a Bayes decision rule. The minimax decision strategy is mixed. It is shown that the optimum decision probabilities are equal to the a posteriori probabilities of the alternatives.
Key concepts: Minimax, Bayes' theorem, Decision rule, A priori and a posteriori, Mathematical optimization, Decision theory, Bayes error rate, Computer science