Estimation of Bounded Normal Mean: An Alternative Proof for the Discreteness of the Least Favorable Prior

Semih Yagli, Alex Dytso, H. Vincent Poor

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

1 Scopus citations

Abstract

This paper studies the classical Bayesian normal mean estimation problem where the estimand is assumed to be contained in a bounded set. It is known that the least favorable distribution for this mean estimation problem is discrete with finitely many mass points. This work offers an alternative proof utilizing the variational diminishing property of Gaussian kernels.

Original languageEnglish (US)
Title of host publication2019 IEEE Information Theory Workshop, ITW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538669006
DOIs
StatePublished - Aug 2019
Event2019 IEEE Information Theory Workshop, ITW 2019 - Visby, Sweden
Duration: Aug 25 2019Aug 28 2019

Publication series

Name2019 IEEE Information Theory Workshop, ITW 2019

Conference

Conference2019 IEEE Information Theory Workshop, ITW 2019
CountrySweden
CityVisby
Period8/25/198/28/19

All Science Journal Classification (ASJC) codes

  • Software
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Information Systems

Cite this

Yagli, S., Dytso, A., & Poor, H. V. (2019). Estimation of Bounded Normal Mean: An Alternative Proof for the Discreteness of the Least Favorable Prior. In 2019 IEEE Information Theory Workshop, ITW 2019 [8988927] (2019 IEEE Information Theory Workshop, ITW 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITW44776.2019.8988927