A model represents the structure of a pattern generator as a set of parameters (each a set of possible values) and computational relations between them, so it can predict patterns.
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A model should approximate a configuration where each possible model state corresponds to a possible pattern, and vice versa, and where the sequence of model states corresponds to the sequence of pattern states.
3:24 PM - 6 Oct 2018
from Cambridge, MA
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