Cargando…
Quantifying Data Dependencies with Rényi Mutual Information and Minimum Spanning Trees
In this study, we present a novel method for quantifying dependencies in multivariate datasets, based on estimating the Rényi mutual information by minimum spanning trees (MSTs). The extent to which random variables are dependent is an important question, e.g., for uncertainty quantification and sen...
Autores principales: | Eggels, Anne, Crommelin, Daan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514583/ https://www.ncbi.nlm.nih.gov/pubmed/33266816 http://dx.doi.org/10.3390/e21020100 |
Ejemplares similares
-
Conditional Rényi Divergence Saddlepoint and the Maximization of α-Mutual Information
por: Cai, Changxiao, et al.
Publicado: (2019) -
A Two-Moment Inequality with Applications to Rényi Entropy and Mutual Information
por: Reeves, Galen
Publicado: (2020) -
Testing Nonlinearity with Rényi and Tsallis Mutual Information with an Application in the EKC Hypothesis
por: Tuna, Elif, et al.
Publicado: (2022) -
Minimum spanning tree analysis of the human connectome
por: van Dellen, Edwin, et al.
Publicado: (2018) -
Data, instance sets, and instances generator for the Hop-Constrained Minimum Spanning Tree problem, the Delay-Constrained Minimum Spanning Tree problem, and their bi-objective variants
por: Carvalho, Iago A., et al.
Publicado: (2023)