Extension of the general linear model to include prior parameter information

Author

Hsieh, Mark C M ; Rayner, Peter J W

Author_Institution

Dept. of Eng., Cambridge Univ., UK

Volume

5

fYear

1997

fDate

21-24 Apr 1997

Firstpage

3569

Abstract

A set of approximations has been applied to allow the inclusion of Gaussian distributed priors for the linear parameters of the general linear model in order that the parameters may be integrated out alongside the Gaussian error noise variance, to give the model evidence and posterior distributions in analytic form. The extended model achieves greater accuracy in parameter estimation and evidence approximation when applied in a Bayesian inference framework, with no increase in computational load

Keywords

Bayes methods; Gaussian distribution; Gaussian noise; approximation theory; filtering theory; parameter estimation; Bayesian inference framework; Gaussian distributed priors; Gaussian error noise variance; evidence approximation; extended model; filtered signal; general linear model; linear parameters; parameter estimation; posterior distribution; prior parameter information; Analysis of variance; Bayesian methods; Equations; Gaussian distribution; Gaussian noise; Laboratories; Least squares approximation; Parameter estimation; Samarium; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on

Conference_Location

Munich

ISSN

1520-6149

Print_ISBN

0-8186-7919-0

Type

conf

DOI

10.1109/ICASSP.1997.604637

Filename

604637