119 citations · 606 across the 12 of their papers we have counts for
15 papers
Understanding interdependency through complex information sharing
Fernando Rosas, Vasilis Ntranos, Christopher J. Ellison +2
The interactions between three or more random variables are often nontrivial, poorly understood, and yet, are paramount for future advances in fields such as network information th…
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…
Exact Complexity: The Spectral Decomposition of Intrinsic Computation
James P. Crutchfield, Christopher J. Ellison, Paul M. Riechers
We give exact formulae for a wide family of complexity measures that capture the organization of hidden nonlinear processes. The spectral decomposition of operator-valued functions…
How Hidden are Hidden Processes? A Primer on Crypticity and Entropy Convergence
John R. Mahoney, Christopher J. Ellison, Ryan G. James +1
We investigate a stationary process's crypticity---a measure of the difference between its hidden state information and its observed information---using the causal states of comput…
Information Symmetries in Irreversible Processes
Christopher J. Ellison, John R. Mahoney, Ryan G. James +2
We study dynamical reversibility in stationary stochastic processes from an information theoretic perspective. Extending earlier work on the reversibility of Markov chains, we focu…
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…
The Past and the Future in the Present
James P. Crutchfield, Christopher J. Ellison
We show how the shared information between the past and future---the excess entropy---derives from the components of directional information stored in the present---the predictive…
Enumerating Finitary Processes
B. D. Johnson, J. P. Crutchfield, C. J. Ellison +1
We show how to efficiently enumerate a class of finite-memory stochastic processes using the causal representation of epsilon-machines. We characterize epsilon-machines in the lang…
Many Roads to Synchrony: Natural Time Scales and Their Algorithms
Ryan G. James, John R. Mahoney, Christopher J. Ellison +1
We consider two important time scales---the Markov and cryptic orders---that monitor how an observer synchronizes to a finitary stochastic process. We show how to compute these ord…
Synchronization and Control in Intrinsic and Designed Computation: An Information-Theoretic Analysis of Competing Models of Stochastic Computation
James P. Crutchfield, Christopher J. Ellison, Ryan G. James +1
We adapt tools from information theory to analyze how an observer comes to synchronize with the hidden states of a finitary, stationary stochastic process. We show that synchroniza…
Information Accessibility and Cryptic Processes: Linear Combinations of Causal States
John R. Mahoney, Christopher J. Ellison, James P. Crutchfield
We show in detail how to determine the time-reversed representation of a stationary hidden stochastic process from linear combinations of its forward-time -machine causal states…
Information Accessibility and Cryptic Processes
John R. Mahoney, Christopher J. Ellison, James P. Crutchfield
We give a systematic expansion of the crypticity--a recently introduced measure of the inaccessibility of a stationary process's internal state information. This leads to a hierarc…
Prediction, Retrodiction, and The Amount of Information Stored in the Present
Christopher J. Ellison, John R. Mahoney, James P. Crutchfield
We introduce an ambidextrous view of stochastic dynamical systems, comparing their forward-time and reverse-time representations and then integrating them into a single time-symmet…
Time's Barbed Arrow: Irreversibility, Crypticity, and Stored Information
James P. Crutchfield, Christopher J. Ellison, John R. Mahoney
We show why the amount of information communicated between the past and future--the excess entropy--is not in general the amount of information stored in the present--the statistic…
Optimal Causal Inference: Estimating Stored Information and Approximating Causal Architecture
Susanne Still, James P. Crutchfield, Christopher J. Ellison
We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate distortion theory to use causal shielding---a natural principle of l…