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    Principal inertia components and applications

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    Date Issued
    2017-08-01
    Publisher Version
    10.1109/TIT.2017.2700857
    Author(s)
    Calmon, F.D.P.
    Makhdoumi, A.
    Medard, M.
    Varia, Mayank
    Christiansen, M.
    Duffy, K.R.
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    Permanent Link
    https://hdl.handle.net/2144/43228
    Version
    First author draft
    Citation (published version)
    F.D.P. Calmon, A. Makhdoumi, M. Medard, M. Varia, M. Christiansen, K.R. Duffy. 2017. "Principal Inertia Components and Applications." IEEE TRANSACTIONS ON INFORMATION THEORY. https://doi.org/10.1109/TIT.2017.2700857
    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 the fundamental limits of perfect privacy.
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    • Hariri Institute Scholarly Works [8]
    • BU Open Access Articles [4751]


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