85 citations · 154 across the 8 of their papers we have counts for
8 papers
Novel Inconsistency Results for Partial Information Decomposition
Philip Hendrik Matthias, Abdullah Makkeh, Michael Wibral +1
We prove that three cornerstone properties of classical information theory, namely non-negativity of information, the chain rule, and invariance under invertible transformations, b…
Shannon invariants: A scalable approach to information decomposition
Aaron J. Gutknecht, Fernando E. Rosas, David A. Ehrlich +3
Distributed systems, such as biological and artificial neural networks, process information via complex interactions engaging multiple subsystems, resulting in high-order patterns…
Characterising high-order interdependence via entropic conjugation
Fernando E. Rosas, Aaron Gutknecht, Pedro A. M. Mediano +1
High-order phenomena play crucial roles in many systems of interest, but their analysis is often highly nontrivial. There is a rich literature providing a number of alternative inf…
From Babel to Boole: The Logical Organization of Information Decompositions
Aaron J. Gutknecht, Abdullah Makkeh, Michael Wibral
The conventional approach to the general Partial Information Decomposition (PID) problem has been redundancy-based: specifying a measure of redundant information between collection…
A partial information decomposition for discrete and continuous variables
Kyle Schick-Poland, Abdullah Makkeh, Aaron J. Gutknecht +3
Conceptually, partial information decomposition (PID) is concerned with separating the information contributions several sources hold about a certain target by decomposing the corr…
Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal Logic
Aaron J. Gutknecht, Michael Wibral, Abdullah Makkeh
Partial information decomposition (PID) seeks to decompose the multivariate mutual information that a set of source variables contains about a target variable into basic pieces, th…
Introducing a differentiable measure of pointwise shared information
Abdullah Makkeh, Aaron J. Gutknecht, Michael Wibral
Partial information decomposition (PID) of the multivariate mutual information describes the distinct ways in which a set of source variables contains information about a target va…
Sampling distribution for single-regression Granger causality estimators
A. J. Gutknecht, L. Barnett
We show for the first time that, under the null hypothesis of vanishing Granger causality, the single-regression Granger-Geweke estimator converges to a generalised distribut…