On Classification of Bivariate Distributions Based on Mutual Information
Mohamed Habibullah, Mohammad Ahsanullah
Abstract
Open-access reader
Mohamed Habibullah, Mohammad Ahsanullah
Abstract
Open-access reader
Among all measures of independence between random variables, mutual information is the only one that is based on information theory. Mutual information takes into account of all kinds of dependencies between variables, i.e., both the linear and non-linear dependencies. In this paper we have classified some well-known bivariate distributions into two classes of distributions based on their mutual information. The distributions within each class have the same mutual information. These distributions have been used extensively as survival distributions of two component systems in reliability theory.
A significance statement is not available in the OpenAlex record.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Among all measures of independence between random variables, mutual information is the only one that is based on information theory. Mutual information takes into account of all kinds of dependencies between variables, i.e., both the linear and non-linear dependencies. In this paper we have classified some well-known bivariate distributions into two classes of distributions based on their mutual information. The distributions within each class have the same mutual information. These distributions have been used extensively as survival distributions of two component systems in reliability theory.
Key concepts: Mutual information, Bivariate analysis, Pointwise mutual information, Mathematics, Independence (probability theory), Total correlation, Random variable, Conditional mutual information