160 citations · 817 across the 25 of their papers we have counts for
7 papers · 1 filter
Unique Information and Secret Key Agreement
Ryan G. James, Jeffrey Emenheiser, James P. Crutchfield
The partial information decomposition (PID) is a promising framework for decomposing a joint random variable into the amount of influence each source variable Xi has on a target va…
A Perspective on Unique Information: Directionality, Intuitions, and Secret Key Agreement
Ryan G. James, Jeffrey Emenheiser, James P. Crutchfield
Recently, the partial information decomposition emerged as a promising framework for identifying the meaningful components of the information contained in a joint distribution. Its…
Multivariate Dependence Beyond Shannon Information
Ryan G. James, James P. Crutchfield
Accurately determining dependency structure is critical to discovering a system's causal organization. We recently showed that the transfer entropy fails in a key aspect of this---…
A new method for choosing parameters in delay reconstruction-based forecast strategies
Joshua Garland, Ryan G. James, Elizabeth Bradley
Delay-coordinate reconstruction is a proven modeling strategy for building effective forecasts of nonlinear time series. The first step in this process is the estimation of good va…
Model-free quantification of time-series predictability
Joshua Garland, Ryan James, Elizabeth Bradley
This paper provides insight into when, why, and how forecast strategies fail when they are applied to complicated time series. We conjecture that the inherent complexity of real-wo…
Intersection Information based on Common Randomness
Virgil Griffith, Edwin K. P. Chong, Ryan G. James +2
The introduction of the partial information decomposition generated a flurry of proposals for defining an intersection information that quantifies how much of "the same information…
Anatomy of a Bit: Information in a Time Series Observation
Ryan G. James, Christopher J. Ellison, James P. Crutchfield
Appealing to several multivariate information measures---some familiar, some new here---we analyze the information embedded in discrete-valued stochastic time series. We dissect th…