Unique Information via Dependency Constraints

no code implementations • 19 Sep 2017 • Ryan G. James, Jeffrey Emenheiser, James P. Crutchfield

The dependency decomposition then allows us to define a measure of the information about a target that can be uniquely attributed to a particular source as the least amount which the source-target statistical dependency can influence the information shared between the sources and the target.

Trimming the Independent Fat: Sufficient Statistics, Mutual Information, and Predictability from Effective Channel States

no code implementations • 7 Feb 2017 • Ryan G. James, John R. Mahoney, James P. Crutchfield

The theoretically ideal implementation is the use of minimal sufficient statistics, where it is well-known that either X or Y can be replaced by their minimal sufficient statistic about the other while preserving the mutual information.

Multivariate Dependence Beyond Shannon Information

no code implementations • 5 Sep 2016 • Ryan G. James, James P. Crutchfield

Accurately determining dependency structure is critical to discovering a system's causal organization.

The Elusive Present: Hidden Past and Future Dependency and Why We Build Models

no code implementations • 2 Jul 2015 • Pooneh M. Ara, Ryan G. James, James P. Crutchfield

When this occurs, the present captures all of the dependency between past and future.

Understanding and Designing Complex Systems: Response to "A framework for optimal high-level descriptions in science and engineering---preliminary report"

no code implementations • 30 Dec 2014 • James P. Crutchfield, Ryan G. James, Sarah Marzen, Dowman P. Varn

We recount recent history behind building compact models of nonlinear, complex processes and identifying their relevant macroscopic patterns or "macrostates".