4 papers
Assessing the Significance of Directed and Multivariate Measures of Linear Dependence Between Time Series
Oliver M. Cliff, Leonardo Novelli, Ben D. Fulcher +2
Inferring linear dependence between time series is central to our understanding of natural and artificial systems. Unfortunately, the hypothesis tests that are used to determine st…
Deriving pairwise transfer entropy from network structure and motifs
Leonardo Novelli, Fatihcan M. Atay, Jürgen Jost +1
Transfer entropy is an established method for quantifying directed statistical dependencies in neuroimaging and complex systems datasets. The pairwise (or bivariate) transfer entro…
Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
Leonardo Novelli, Patricia Wollstadt, Pedro Mediano +2
Network inference algorithms are valuable tools for the study of large-scale neuroimaging datasets. Multivariate transfer entropy is well suited for this task, being a model-free m…
IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks
Patricia Wollstadt, Joseph T. Lizier, Raul Vicente +5
The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using…