Skip to Main content Skip to Navigation
Conference papers

A NEW DERIVATION OF THE BAYESIAN BOUNDS FOR PARAMETER ESTIMATION

Abstract : This paper deals with minimal bounds in the Bayesian context. We express the minimum mean square error of the conditional mean estimator as the solution of a continuum constrained optimization problem. And, by relaxing these constraints, we obtain the bounds of the Weiss-Weinstein family. Moreover, this method enables us to derive new bounds as the Bayesian version of the deterministic Abel bound.
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal.inria.fr/inria-00444830
Contributor : Alexandre Renaux <>
Submitted on : Thursday, January 7, 2010 - 12:50:04 PM
Last modification on : Monday, February 15, 2021 - 10:50:53 AM
Long-term archiving on: : Friday, June 18, 2010 - 12:31:55 AM

File

RFL05a.pdf
Publisher files allowed on an open archive

Identifiers

  • HAL Id : inria-00444830, version 1

Citation

Alexandre Renaux, Philippe Forster, Pascal Larzabal. A NEW DERIVATION OF THE BAYESIAN BOUNDS FOR PARAMETER ESTIMATION. IEEE Workshop on Statistical Signal Processing, SSP-2005, 2005, Bordeaux, France. ⟨inria-00444830⟩

Share

Metrics

Record views

501

Files downloads

470