By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
T. L. Lai ; Dept. of Stat., Stanford Univ., CA, USA

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

Published in:

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

Date of Conference: