A Class of Lower Bounds for Bayesian Risk with a Bregman Loss

Alex Dytso, Michael Faus, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A general class of Bayesian lower bounds when the underlying loss function is a Bregman divergence is demonstrated. This class can be considered as an extension of the Weinstein-Weiss family of bounds for the mean squared error and relies on finding a variational characterization of Bayesian risk. The approach allows for the derivation of a version of the Cramér-Rao bound that is specific to a given Bregman divergence. The effectiveness of the new bound is evaluated in the Poisson noise setting.

Original languageEnglish (US)
Title of host publication2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728154787
DOIs
StatePublished - May 2020
Event21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020 - Atlanta, United States
Duration: May 26 2020May 29 2020

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2020-May

Conference

Conference21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020
CountryUnited States
CityAtlanta
Period5/26/205/29/20

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

Cite this

Dytso, A., Faus, M., & Poor, H. V. (2020). A Class of Lower Bounds for Bayesian Risk with a Bregman Loss. In 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020 [9154314] (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPAWC48557.2020.9154314