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

Maximum likelihood estimation of K-distribution parameters via the expectation-maximization algorithm

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

2 Author(s)
Roberts, William J.J. ; Inf. Technol. Div., Defence Sci. & Technol. Organ., Salisbury, SA, Australia ; Furui, S.

Maximum likelihood (ML) estimates of K-distribution parameters are derived using the expectation maximization (EM) approach. This approach demonstrates the computational advantages compared with 2-D numerical maximization of the likelihood function using a Nelder-Mead approach. For large datasets, the EM approach yields more accurate estimates than those of a non-ML estimation technique.

Published in:

Signal Processing, IEEE Transactions on  (Volume:48 ,  Issue: 12 )

Date of Publication:

Dec 2000

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.