Inexact Bayesian estimation

Mohamed Abdel-Mottaleb, Azriel Rosenfeld

Research output: Contribution to journalArticle

3 Citations (Scopus)

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
Pages (from-to)641-646
Number of pages6
JournalPattern Recognition
Volume25
Issue number6
DOIs
StatePublished - Jan 1 1992
Externally publishedYes

Fingerprint

Probability density function
Computer vision

Keywords

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

ASJC Scopus subject areas

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

Cite this

Inexact Bayesian estimation. / Abdel-Mottaleb, Mohamed; Rosenfeld, Azriel.

In: Pattern Recognition, Vol. 25, No. 6, 01.01.1992, p. 641-646.

Research output: Contribution to journalArticle

Abdel-Mottaleb, Mohamed ; Rosenfeld, Azriel. / Inexact Bayesian estimation. In: Pattern Recognition. 1992 ; Vol. 25, No. 6. pp. 641-646.
@article{16258ed170d943699ed95d814a4cadae,
title = "Inexact Bayesian estimation",
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.",
keywords = "Approximate priors, Bayesian estimation, Hyperpriors, Second-stage priors",
author = "Mohamed Abdel-Mottaleb and Azriel Rosenfeld",
year = "1992",
month = "1",
day = "1",
doi = "10.1016/0031-3203(92)90080-3",
language = "English",
volume = "25",
pages = "641--646",
journal = "Pattern Recognition",
issn = "0031-3203",
publisher = "Elsevier Limited",
number = "6",

}

TY - JOUR

T1 - Inexact Bayesian estimation

AU - Abdel-Mottaleb, Mohamed

AU - Rosenfeld, Azriel

PY - 1992/1/1

Y1 - 1992/1/1

N2 - 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.

AB - 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.

KW - Approximate priors

KW - Bayesian estimation

KW - Hyperpriors

KW - Second-stage priors

UR - http://www.scopus.com/inward/record.url?scp=29944434296&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=29944434296&partnerID=8YFLogxK

U2 - 10.1016/0031-3203(92)90080-3

DO - 10.1016/0031-3203(92)90080-3

M3 - Article

VL - 25

SP - 641

EP - 646

JO - Pattern Recognition

JF - Pattern Recognition

SN - 0031-3203

IS - 6

ER -