Conditional multivariate mutual information measures

Authors

  • Nguyễn Quỳnh Diệp ĐH Sư Phạm HN
  • Phạm Thọ Hoàn ĐH Sư Phạm HN
  • Hồ Tú Bảo Viện KH&CN tiên tiến Nhật Bản

DOI:

https://doi.org/10.15625/1813-9663/30/2/3350

Keywords:

Information theory, entropy, mutual information, biological network reconstruction.

Abstract

Mutual information of two variables is a measure of relationship between two variables: the larger this measure the stronger the dependence, and vice visa. However, mutual information does not indicate whether the relationship between the variables is direct or indirect. To detect "direct mutual relations", we can use conditional mutual information.

In the previous studies, we have proposed the mutual information measures of multiple variables. There are many mutual information measures with more than two variables. Each of them is sensitive to a kind of relationships that may exist among the multiple variables. However, as mutual information of two variables, the multivariate mutual information measures do not show whether the multivariate relationships are direct or indirect. In this paper, we propose new multivariate conditional mutual information measures and show that they can detect indirect multivariate relationships through conditional variables.

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Author Biographies

Nguyễn Quỳnh Diệp, ĐH Sư Phạm HN

Phạm Thọ Hoàn, ĐH Sư Phạm HN

Hồ Tú Bảo, Viện KH&CN tiên tiến Nhật Bản

Published

2014-06-10

How to Cite

[1]
N. Q. Diệp, P. T. Hoàn, and H. T. Bảo, “Conditional multivariate mutual information measures”, JCC, vol. 30, no. 2, pp. 117–126, Jun. 2014.

Issue

Section

Computer Science