The formulation of the Partial Information Decomposition (PID) framework by Williams and
Beer in 2010 attracted a significant amount of attention to the problem of defining redundant …

The problem of how to properly quantify redundant information is an open question that has
been the subject of much recent research. Redundant information refers to information about …

The Partial Information Decomposition (PID) framework has emerged as a powerful tool for
analyzing high-order interdependencies in complex network systems. However, its …

What are the distinct ways in which a set of predictor variables can provide information about
a target variable? When does a variable provide unique information, when do variables …

Causality lies at the heart of scientific inquiry, serving as the fundamental basis for
understanding interactions among variables in physical systems. Despite its central role …

Abstract Information theoretic measures can be used to identify nonlinear interactions
between source and target variables through reductions in uncertainty. In information …

Describing statistical dependencies is foundational to empirical scientific research. For
uncovering intricate and possibly nonlinear dependencies between a single target variable …

We consider the “partial information decomposition”(PID) problem, which aims to
decompose the information that a set of source random variables provide about a target …

In this work we study the distributed representations learnt by generative neural network
models. In particular, we investigate the properties of redundant and synergistic information …

In an ecohydrologic system, components of atmospheric, vegetation, and root‐soil
subsystems participate in forcing and feedback interactions at varying time scales and …