The penalty term of Exponentially Embedded Family is estimated mutual information

Zhenghan Zhu; Steven Kay

ICASSP 2017accepted0 citations

The penalty term of Exponentially Embedded Family is estimated mutual information

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.

BibTeX

@inproceedings{icassp2017_thepenaltytermof,
  title = {The penalty term of Exponentially Embedded Family is estimated mutual information},
  author = {Zhenghan Zhu and Steven Kay},
  booktitle = {ICASSP 2017},
  year = {2017}
}