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Estimation of mutual information using copula density function

Zeng, X. and Durrani, T. S. (2011) Estimation of mutual information using copula density function. Electronics Letters, 47 (8). pp. 493-494. ISSN 0013-5194

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Abstract

The dependence between random variables may be measured by mutual information. However, the estimation of mutual information is difficult since the estimation of the joint probability density function (PDF) of non-Gaussian distributed data is a hard problem. Copulas offer a natural approach for estimating mutual information, since the joint probability density function of random variables can be expressed as the product of the associated copula density function and marginal PDFs. The experiment demonstrates that the proposed copulas-based mutual information is much more accurate than conventional methods such as the joint histogram and Parzen window based mutual information that are widely used in image processing.

Item type: Article
ID code: 40544
Keywords: estimation, mutual information, copula, density function, gaussian processes, signal processing , information theory , Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
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Depositing user: Pure Administrator
Date Deposited: 25 Jul 2012 13:49
Last modified: 27 Mar 2014 10:20
URI: http://strathprints.strath.ac.uk/id/eprint/40544

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