Title :
Parameter estimation using Volterra series
Author :
Hsieh, Murk C M ; Rayner, P.J.W.
Author_Institution :
Dept. of Eng., Cambridge Univ., UK
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;
Conference_Titel :
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-4428-6
DOI :
10.1109/ICASSP.1998.681619
