The bayesian abel bound on the mean square error

Alexandre Renaux, Philippe Forster, Pascal Larrabal, Christ Richmond

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper deals with lower bound on the Mean Square Error (MSE). In the Bayesian framework, we present a new bound which is derived from a constrained optimization problem. This bound is found to be tighter than the Bayesian Bhattacharyya bound, the Reuven-Messer bound, the Bobrovsky-Zakaï bound, and the Bayesian Cramér-Rao bound.

Original languageEnglish (US)
Title of host publicationBayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
PublisherWiley-IEEE Press
Pages176-179
Number of pages4
ISBN (Electronic)9780470544198
ISBN (Print)0470120959, 9780470120958
DOIs
StatePublished - Jan 1 2007

Keywords

  • Bayesian methods
  • Estimation error
  • Mean square error methods
  • Optimization
  • Parameter estimation
  • Signal to noise ratio

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Renaux, A., Forster, P., Larrabal, P., & Richmond, C. (2007). The bayesian abel bound on the mean square error. In Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking (pp. 176-179). Wiley-IEEE Press. https://doi.org/10.1109/9780470544198.ch12