Cart (Loading....) | Create Account
Close category search window
 

Time discretization of continuous-time filters for hidden Markov model parameter estimation

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
$31 $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

3 Author(s)
James, M.R. ; Dept. of Syst. Eng., Australian Nat. Univ., Canberra, ACT, Australia ; Krishnamurthy, V. ; Le Gland, F.

The authors propose numerical techniques for parameter estimation of fast-sampled homogeneous Markov chains observed in white Gaussian noise. Continuous-time filters that estimate the quantities used in the expectation-maximization (EM) algorithm for maximum likelihood parameter estimation have been obtained by R.J. Elliott (1991, 1992). The numerical work is based on the robust discretization of these filters. The advantage of using filters in the EM algorithm is that they have negligible memory requirements, independent of the number of observations. In comparison, standard discrete-time EM algorithms (Baum-Welch re-estimation equations) are based on smoothers and require the use of the forward-backward algorithm, which is a fixed-interval algorithm and has memory requirements proportional to the number of observations. Although the computational complexity of the filters at each time instant is O(N4) (for a N state Markov) compared to O(N2) for the forward-backward scheme, the filters are suitable for parallel implementation. Simulations are presented to illustrate the satisfactory performance of the algorithms

Published in:

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

Date of Conference:

1992

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.