RT info:eu-repo/semantics/article
T1 IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks
A1 Wollstadt, Patricia
A1 Lizier, Joseph
A1 Vicente, Raul
A1 Finn, Conor
A1 Martínez Zarzuela, Mario
A1 Mediano, Pedro
A1 Novelli, Leonardo
A1 Wibral, Michael
AB We present IDTxl (the Information Dynamics Toolkit xl), a new open source Python toolbox for effective network inference from multivariate time series using information theory, available from GitHub (https://github.com/pwollstadt/IDTxl).Information theory (Cover & Thomas, 2006; MacKay, 2003; Shannon, 1948) is the math- ematical theory of information and its transmission over communication channels. In- formation theory provides quantitative measures of the information content of a single random variable (entropy) and of the information shared between two variables (mutual information). The defined measures build on probability theory and solely depend on the probability distributions of the variables involved. As a consequence, the dependence between two variables can be quantified as the information shared between them, without the need to explicitly model a specific type of dependence. Hence, mutual information is a model-free measure of dependence, which makes it a popular choice for the analysis of systems other than communication channels.
PB Open Journals
SN 2475-9066
LK https://uvadoc.uva.es/handle/10324/64344
UL https://uvadoc.uva.es/handle/10324/64344
LA eng
NO Wollstadt et al., (2019). IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks. Journal of Open Source Software, 4(34), 1081, https://doi.org/10.21105/joss.01081
NO Producción Científica
DS UVaDOC
RD 04-dic-2024