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A Geometrical Interpretation of Exponentially Embedded Families of Gaussian Probability Density Functions for Model Selection | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

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 ...Show More

Abstract:

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: IEEE Transactions on Signal Processing ( Volume: 61, Issue: 1, January 2013)
Page(s): 62 - 67
Date of Publication: 04 October 2012

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