Operational interpretation of Rényi conditional mutual information via composite hypothesis testing against Markov distributions

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Conference Proceeding
Proceedings IEEE International Symposium on Information Theory (ISIT 2016), 2016, pp. 585 - 589
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We revisit the problem of asymmetric binary hypothesis testing against a composite alternative hypothesis. We introduce a general framework to treat such problems when the alternative hypothesis adheres to certain axioms. In this case we find the threshold rate, the optimal error and strong converse exponents (at large deviations from the threshold) and the second-order asymptotics (at small deviations from the threshold). We apply our results to find operational interpretations of Rényi information measures. In particular, in case the alternative hypothesis consists of certain tripartite distributions satisfying the Markov property, we find that the optimal exponents are determined by the Rényi conditional mutual information
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