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- W2952866921 abstract "We explore properties and applications of the Principal Inertia Components (PICs) between two discrete random variables $X$ and $Y$. The PICs lie in the intersection of information and estimation theory, and provide a fine-grained decomposition of the dependence between $X$ and $Y$. Moreover, the PICs describe which functions of $X$ can or cannot be reliably inferred (in terms of MMSE) given an observation of $Y$. We demonstrate that the PICs play an important role in information theory, and they can be used to characterize information-theoretic limits of certain estimation problems. In privacy settings, we prove that the PICs are related to fundamental limits of perfect privacy." @default.
- W2952866921 created "2019-06-27" @default.
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- W2952866921 date "2017-04-03" @default.
- W2952866921 modified "2023-09-26" @default.
- W2952866921 title "Principal Inertia Components and Applications" @default.
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