Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

A Geometrical Interpretation of Exponentially Embedded Families of Gaussian Probability Density Functions for Model Selection

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)
Costa, R. ; Naval Undersea Warfare Center Newport, Newport, RI, USA ; Kay, S.

Model selection via exponentially embedded families (EEF) of probability models has been shown to perform well on many practical problems of interest. A key component in utilizing this approach is the definition of a model origin (i.e. null hypothesis) which is embedded individually within each competing model. In this correspondence we give a geometrical interpretation of the EEF and study the sensitivity of the EEF approach to the choice of model origin in a Gaussian hypothesis testing framework. We introduce the information center (I-center) of competing models as an origin in this procedure and compare this to using the standard null hypothesis. Finally we derive optimality conditions for which the EEF using I-center achieves optimal performance in the Gaussian hypothesis testing framework.

Published in:

Signal Processing, IEEE Transactions on  (Volume:61 ,  Issue: 1 )