Title :
A note on feeding uncertain knowledge into recursive least squares
Author_Institution :
Inst. of Inf. Theory & Autom., Czechoslovak Acad. of Sci., Prague, Czechoslovakia
Abstract :
The author presents a method to incorporate available uncertain prior knowledge into recursive least squares (RLSs) initial conditions. The simple procedure makes it possible to incorporate vague prior information about a linear combination of regression coefficients. It translates such knowledge as a guess of static gain both into a point estimate and prior covariance. The information about an imprecisely known linear relation of unknown parameters is the key result
Keywords :
least squares approximations; parameter estimation; statistics; initial conditions; least squares approximations; linear combination; parameter estimation; point estimate; prior covariance; recursive least squares; regression coefficients; static gain; uncertain knowledge; vague prior information; Adaptive control; Automation; Bayesian methods; Dispersion; Information theory; Least squares approximation; Least squares methods; Parameter estimation; Resonance light scattering; Statistics;
Conference_Titel :
Decision and Control, 1991., Proceedings of the 30th IEEE Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-0450-0
DOI :
10.1109/CDC.1991.261469