Survey on the Estimation of Mutual Information Methods as a Measure of Dependency Versus Correlation Analysis

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Survey on the Estimation of Mutual Information Methods as a Measure of Dependency Versus Correlation Analysis

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Title: Survey on the Estimation of Mutual Information Methods as a Measure of Dependency Versus Correlation Analysis
Author(s):
Gencaga, Deniz (UT Dallas);
Malakar, Nabin K. (UT Dallas);
Lary, David J. (UT Dallas)
Item Type: Article
Keywords: Show Keywords
Abstract: In this survey, we present and compare different approaches to estimate Mutual Information (MI) from data to analyse general dependencies between variables of interest in a system. We demonstrate the performance difference of MI versus correlation analysis, which is only optimal in case of linear dependencies. First, we use a piece-wise constant Bayesian methodology using a general Dirichlet prior. In this estimation method, we use a two-stage approach where we approximate the probability distribution first and then calculate the marginal and joint entropies. Here, we demonstrate the performance of this Bayesian approach versus the others for computing the dependency between different variables. We also compare these with linear correlation analysis. Finally, we apply MI and correlation analysis to the identification of the bias in the determination of the aerosol optical depth (AOD) by the satellite based Moderate Resolution Imaging Spectroradiometer (MODIS) and the ground based AErosol RObotic NETwork (AERONET). Here, we observe that the AOD measurements by these two instruments might be different for the same location. The reason of this bias is explored by quantifying the dependencies between the bias and 15 other variables including cloud cover, surface reflectivity and others.
ISSN: 0094-243X
Persistent Link: http://hdl.handle.net/10735.1/4951
http://dx.doi.org/10.1063/1.4903714
Bibliographic Citation: Gencaga, D., N. K. Malakar, and D. J. Lary. 2014. "Survey on the estimation of mutual information methods as a measure of dependency versus correlation analysis." AIP Conference Proceedings 1636, doi:10.1063/1.4903714.
Terms of Use: ©2014 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics.

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