A method for approximating the density of maximum-likelihood and maximum a posteriori estimates under a Gaussian noise model

Craig K. Abbey, Eric Clarkson, Harrison H. Barrett, Stefan P. Müller, Frank J. Rybicki

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

The performance of maximum-likelihood (ML) and maximum a posteriori (MAP) estimates in non-linear problems at low data SNR is not well predicted by the Cramér-Rao or other lower bounds on variance. In order to better characterize the distribution of ML and MAP estimates under these conditions, we derive a point approximation to density values of the conditional distribution of such estimates. In an example problem, this approximate distribution captures the essential features of the distribution of ML estimates in the presence of Gaussian-distributed noise.

Original languageEnglish (US)
Pages (from-to)395-403
Number of pages9
JournalMedical Image Analysis
Volume2
Issue number4
DOIs
StatePublished - Jan 1 1998

Keywords

  • Cramér-Rao bound
  • Maximum-likelihood estimation
  • Quantitation

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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