Abstract
The penalty term plays an important role in model order selection rules. The Exponentially Embedded Families (EEF) is consistent and effective in model order selection. In this paper we show that the EEF penalty term can be viewed as estimated mutual information (MI) between unknown parameters and received data from Bayesian viewpoints. The finding is a result of an important relationship between Kullback-Leibler Divergence (KLD), signal-to-noise ratio (SNR) and MI in estimation/detection of random signals, which is also introduced.
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