Cargando…
Mutual Information between Discrete and Continuous Data Sets
Mutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with “binning” when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimator for the...
Autor principal: | Ross, Brian C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3929353/ https://www.ncbi.nlm.nih.gov/pubmed/24586270 http://dx.doi.org/10.1371/journal.pone.0087357 |
Ejemplares similares
-
Estimating the Mutual Information between Two Discrete, Asymmetric Variables with Limited Samples
por: Hernández, Damián G., et al.
Publicado: (2019) -
Mutual Information between Discrete Variables with Many Categories using Recursive Adaptive Partitioning
por: Seok, Junhee, et al.
Publicado: (2015) -
Approximations of Shannon Mutual Information for Discrete Variables with Applications to Neural Population Coding
por: Huang, Wentao, et al.
Publicado: (2019) -
Adaptation of Partial Mutual Information from Mixed Embedding to Discrete-Valued Time Series
por: Papapetrou, Maria, et al.
Publicado: (2022) -
Discretization and Feature Selection Based on Bias Corrected Mutual Information Considering High-Order Dependencies
por: Roy, Puloma, et al.
Publicado: (2020)