An Inequality for Bayesian Bregman Risks with Applications in Directional Estimation

Michael Faub; Alex Dytso; H. Vincent Poor

doi:10.1109/MFI52462.2021.9591193

An Inequality for Bayesian Bregman Risks with Applications in Directional Estimation

Michael Faub, Alex Dytso, H. Vincent Poor

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

An inequality connecting the Bayesian Bregman risk, the Kullback-Leibler divergence and distributions from the exponential family is derived. The inequality has applications in directional and robust estimation and can provide universal lower bounds on Bregman risks. Its usefulness is illustrated using the example of estimation in Poisson noise with a logarithmic cost function.

Original language	English (US)
DOIs	https://doi.org/10.1109/MFI52462.2021.9591193
State	Published - 2021
Externally published	Yes
Event	2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2021 - Karlsruhe, Germany Duration: Sep 23 2021 → Sep 25 2021

Conference

Conference	2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2021
Country/Territory	Germany
City	Karlsruhe
Period	9/23/21 → 9/25/21

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Software
Computer Science Applications

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10.1109/MFI52462.2021.9591193

Cite this

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title = "An Inequality for Bayesian Bregman Risks with Applications in Directional Estimation",

abstract = "An inequality connecting the Bayesian Bregman risk, the Kullback-Leibler divergence and distributions from the exponential family is derived. The inequality has applications in directional and robust estimation and can provide universal lower bounds on Bregman risks. Its usefulness is illustrated using the example of estimation in Poisson noise with a logarithmic cost function.",

author = "Michael Faub and Alex Dytso and {Vincent Poor}, H.",

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AU - Dytso, Alex

AU - Vincent Poor, H.

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N2 - An inequality connecting the Bayesian Bregman risk, the Kullback-Leibler divergence and distributions from the exponential family is derived. The inequality has applications in directional and robust estimation and can provide universal lower bounds on Bregman risks. Its usefulness is illustrated using the example of estimation in Poisson noise with a logarithmic cost function.

AB - An inequality connecting the Bayesian Bregman risk, the Kullback-Leibler divergence and distributions from the exponential family is derived. The inequality has applications in directional and robust estimation and can provide universal lower bounds on Bregman risks. Its usefulness is illustrated using the example of estimation in Poisson noise with a logarithmic cost function.

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U2 - 10.1109/MFI52462.2021.9591193

DO - 10.1109/MFI52462.2021.9591193

M3 - Paper

AN - SCOPUS:85122898977

T2 - 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2021

Y2 - 23 September 2021 through 25 September 2021

ER -

An Inequality for Bayesian Bregman Risks with Applications in Directional Estimation

Abstract

Conference

All Science Journal Classification (ASJC) codes

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