Pattern, Prediction and Computation in One-Dimensional Sequences

Nonlinear Time Series Analysis Seminar

Max Planck Institute for the Physics of Complex Systems

January 2005


Understanding and quantifying patterns in physical systems is a significant challenge for scientists. Borrowing ideas from the formal theory languages and information theory, computational mechanics provides a framework to express and analyze one-dimensional patterns. The resulting description of the pattern, called an epsilon-machine or casual state machine, represents a complete statistical characterization of the pattern. From the epsilon-machine, measures of computation, such as statistical complexity and entropy, are calculable. I will consider a specific example from condensed matter physics where epsilon-machine reconstruction gives new insight into the structure of a layered material.

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