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A logical way of making a model more closely fit observed behaviour is to augment its dimensions by adding a hidden process that somehow affects the evolution of the principal observable value. The complete model specification then acquires the parameters of the hidden process and its possible correlation with the principal process. This also requires a description of how the hidden process starts, which is the main subject of this paper.
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