Parameter estimation using Volterra series
暂无分享，去创建一个
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.
[1] William H. Press,et al. Numerical Recipes in C, 2nd Edition , 1992 .
[2] D. Mackay,et al. Bayesian methods for adaptive models , 1992 .
[3] L. M. M.-T.. Theory of Probability , 1929, Nature.
[4] K. Riedel. Numerical Bayesian Methods Applied to Signal Processing , 1996 .
[5] Peter J. W. Rayner,et al. Extension of the general linear model to include prior parameter information , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.