Parameter estimation using Volterra series

Author

Hsieh, Murk C M ; Rayner, P.J.W.

Author_Institution

Dept. of Eng., Cambridge Univ., UK

Volume

4

fYear

1998

fDate

12-15 May 1998

Firstpage

2341

Abstract

A polynomial approximation to the likelihood function allows for marginalised estimates of model parameters to be obtained in the form of a Volterra series. The series can be applied directly to the observed data vector in an iterative fashion, to converge upon a set of parameter MAP estimates with low computational cost. A sample application towards OCR is used as an illustration

Keywords

Bayes methods; Volterra series; approximation theory; convergence of numerical methods; iterative methods; maximum likelihood estimation; object recognition; optical character recognition; parameter estimation; polynomials; Bayesian analysis; MAP estimates; OCR; Volterra series; convergence; iterative method; likelihood function; low computational cost; marginalised estimates; model parameters; object recognition; observed data vector; optical character recognition; parameter estimation; polynomial approximation; Bayesian methods; Computational efficiency; Data models; Equations; Laboratories; Optical character recognition software; Parameter estimation; Polynomials; Predictive models; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1520-6149

Print_ISBN

0-7803-4428-6

Type

conf

DOI

10.1109/ICASSP.1998.681619

Filename

681619