Entropy | |
Improvement of the |
|
Agata Charzyńska2  Anna Gambin1  | |
[1]Institute of Informatics, University of Warsaw, Banacha Street 2, 02-097 Warsaw, Poland | |
[2]Institute of Computer Science Polish Academy of Sciences, Jana Kazimierza Street 5, 01-248 Warsaw, Poland | |
关键词:
differential entropy;
|
|
DOI : 10.3390/e18010013 | |
来源: mdpi | |
【 摘 要 】
In this paper, we investigate efficient estimation of differential entropy for multivariate random variables. We propose bias correction for the nearest neighbor estimator, which yields more accurate results in higher dimensions. In order to demonstrate the accuracy of the improvement, we calculated the corrected estimator for several families of random variables. For multivariate distributions, we considered the case of independent marginals and the dependence structure between the marginal distributions described by Gaussian copula. The presented solution may be particularly useful for high dimensional data, like those analyzed in the systems biology field. To illustrate such an application, we exploit differential entropy to define the robustness of biochemical kinetic models.
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO202003190000787ZK.pdf | 1121KB | download |