By Topic

Adaptive Probability Distribution Estimation Based upon Maximum Entropy

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

3 Author(s)
Miller, James E. ; AFIS/IND; Bolling AFB; Washington DC 20332 USA. ; Kulp, Richard W. ; Orr, George E.

Our ad-hoc adaptive estimation procedure for the probability distribution of a continuous random variable is based upon the Shannon-Jaynes maximum entropy concept and uses regression techniques or the Kullback-Leibler Divergence measure of information variation to select the appropriate functions for fitting a regular exponential family distribution to the data. This parametric estimation technique uses the data to select the probability distribution and estimate the parameters of the distribution. It is not known how this technique compares to other parametric techniques (eg, maximum likelihood) when the underlying distribution is known. However, this procedure is reasonable when the underlying distribution is not known. The scheme has been tested against known distributions with excellent results.

Published in:

Reliability, IEEE Transactions on  (Volume:R-33 ,  Issue: 4 )