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 ∈-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.
complexity computation entropy information pattern statistical mechanics causal state ∈-machine
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