Computational Mechanics: Pattern and Prediction, Structure and Simplicity

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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 e-machine--is the minimal one consistent with accurate prediction. We establish several results on e-machine optimality and uniqueness and on how e-machines compare to alternative representations. Further results relate measures of randomness and structural complexity obtained from e-machines to those from ergodic and information theories.

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