Bayesian estimation of bessel K form random vectors in AWGN

Pavel A. Khazron, Ivan Selesnick

Research output: Contribution to journalArticle

Abstract

We present new Bayesian estimators for spherically-contoured Bessel K form (BKF) random vectors in additive white Gaussian noise (AWGN). The derivations are an extension of existing results for the scalar BKF and multivariate Laplace (MLAP) densities. MAP and MMSE estimators are derived. We show that the MMSE estimator can be written in exact form in terms of the generalized incomplete Gamma function. Computationally efficient approximations are given. We compare the proposed exact and approximate MMSE estimators with recent results using the BKF density, both in terms of the shrinkage rules and the associated mean-square error.

Original languageEnglish (US)
Pages (from-to)261-264
Number of pages4
JournalIEEE Signal Processing Letters
Volume15
DOIs
StatePublished - 2008

Fingerprint

Bayesian Estimation
Friedrich Wilhelm Bessel
Gaussian White Noise
Random Vector
Mean square error
Minimum Mean Square Error
Estimator
Incomplete gamma Function
Bayesian Estimator
Shrinkage
Laplace
Scalar
Form
Approximation

Keywords

  • Bayesian estimation
  • Bessel K form density
  • MAP estimator
  • MMSE estimator
  • Wavelet denoising

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

Bayesian estimation of bessel K form random vectors in AWGN. / Khazron, Pavel A.; Selesnick, Ivan.

In: IEEE Signal Processing Letters, Vol. 15, 2008, p. 261-264.

Research output: Contribution to journalArticle

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