Inexact Bayesian estimation

Mohamed Abdel-Mottaleb, Azriel Rosenfeld

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Bayesian estimation has many applications in computer vision. A frequent objection to Bayesian estimation is that the probability density functions (pdfs) involved are usually not known exactly. In fact, exact knowledge of the pdfs is not important; it often suffices to know the pdfs approximately. Furthermore, it may even suffice if we have a family of pdfs one of which approximates the actual pdf, provided we specify a "second-stage" pdf on the family such that the approximation of the actual pdf has high probability.

Original languageEnglish (US)
Pages (from-to)641-646
Number of pages6
JournalPattern Recognition
Volume25
Issue number6
DOIs
StatePublished - Jun 1992
Externally publishedYes

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Keywords

  • Approximate priors
  • Bayesian estimation
  • Hyperpriors
  • Second-stage priors

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

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