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

Strongly Consistent Estimation of the Sample Distribution of Noisy Continuous-Parameter Fields

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)
Kovalsky, S.Z. ; Dept. of Electr. & Comput. Eng., Ben-Gurion Univ., Beer-Sheva, Israel ; Cohen, G. ; Francos, J.M.

The general problem of defining and determining the sample distribution in the case of continuous-parameter random fields is addressed. Defining a distribution in the case of deterministic functions is straightforward, based on measures of sublevel sets. However, the fields we consider are the sum of a deterministic component (nonrandom multidimensional function) and an i.i.d. random field; an attempt to extend the same notion to the stochastic case immediately raises some fundamental difficulties. We show that by “uniformly sampling” such random fields the difficulties may be avoided and a sample distribution may be compatibly defined and determined. Not surprisingly, the obtained result resembles the known fact that the probability distribution of the sum of two independent random variables is the convolution of their distributions. Finally, we apply the results to derive a solution to the problem of deformation estimation of one- and multidimensional signals in the presence of measurement noise.

Published in:

Information Theory, IEEE Transactions on  (Volume:57 ,  Issue: 3 )

Date of Publication:

March 2011

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.