Abstract
Computational mechanics, an approach to structural complexity, defines a process's causal states and gives a procedure for finding them. We show that the causal-state representation—an ∈-machine—is the minimal one consistent with accurate prediction. We establish several results on ∈-machine optimality and uniqueness and on how ∈-machines compare to alternative representations. Further results relate measures of randomness and structural complexity obtained from ∈-machines to those from ergodic and information theories.
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Shalizi, C.R., Crutchfield, J.P. Computational Mechanics: Pattern and Prediction, Structure and Simplicity. Journal of Statistical Physics 104, 817–879 (2001). https://doi.org/10.1023/A:1010388907793
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DOI: https://doi.org/10.1023/A:1010388907793