Title :
An empirical Bayes approach to modeling and control of stochastic systems with time-varying parameters
Author_Institution :
Dept. of Stat., Stanford Univ., CA, USA
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;
Conference_Titel :
Decision and Control, 1992., Proceedings of the 31st IEEE Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-0872-7
DOI :
10.1109/CDC.1992.371552
