Publication

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Michael Christoph Gastpar, Amedeo Roberto Esposito
2022
Conference Papers

Abstract

We consider the problem of parameter estimation in a Bayesian setting and propose a general lower-bound that includes part of the family of f-Divergences. The results are then applied to specific settings of interest and compared to other notable results in the literature. In particular, we show that the known bounds using Mutual Information can be improved by using, for example, Maximal Leakage, Hellinger divergence, or generalizations of the Hockey-Stick divergence.

Official source

https://infoscience.epfl.ch/entities/publication/cdc291a0-eb4e-4950-91b1-2bc5914cb6a9

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Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

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