An empirical Bayes approach to modeling and control of stochastic systems with time-varying parameters

Author

Lai, Tze Leung

Author_Institution

Dept. of Stat., Stanford Univ., CA, USA

fYear

1992

fDate

1992

Firstpage

1072

Abstract

An empirical Bayes approach is proposed for modeling the dynamics of unknown parameters, which may undergo both regular fluctuations and erratic changes over time, in stochastic regression models and linear stochastic difference equations. A rich and flexible class of empirical Bayes models of parameter dynamics is shown to lead to tractable recursive algorithms for estimating the time-varying parameters with good statistical properties. Applications of these recursive estimators to developing adaptive controllers of certainty-equivalence type are also discussed

Keywords

Bayes methods; adaptive control; dynamics; linear differential equations; stochastic systems; time-varying systems; adaptive controllers; certainty-equivalence type; empirical Bayes approach; linear stochastic difference equations; modeling; recursive estimators; stochastic regression models; stochastic systems; time-varying parameters; Adaptive control; Bayesian methods; Bismuth; Difference equations; Fluctuations; Moment methods; Parameter estimation; Probability density function; Programmable control; Recursive estimation; Statistics; Stochastic processes; Yttrium;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1992., Proceedings of the 31st IEEE Conference on

Conference_Location

Tucson, AZ

Print_ISBN

0-7803-0872-7

Type

conf

DOI

10.1109/CDC.1992.371552

Filename

371552