The entropic upper bound for Bayes risk in a general quantum case is presented. We obtained generalization of the entropic Lower bound for probability of detection. Our result indicates upper bound for Bayes risk (in a particular case of loss function – for probability of detection) in a pretty general setting of an arbitrary finite von Neumann algebra. It is also shown under which condition the indicated upper bound is achieved.
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The paper provides several lower bounds and an upper bound for the Bayes risk in statistical prediction theory. The bounds depend on the Fisher information or the bias of the Bayes predictor. The results improve and extend the inequalities of Brown and Gajek (1990), Takada (1999) and Koike (1999). As an application we evaluate the minimax risk in a problem of sequential prediction.
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