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MiS Preprint

On the Generative Nature of Prediction

Wolfgang Löhr and Nihat Ay


Given an observed stochastic process, computational mechanics provides an explicit and efficient method of constructing a minimal hidden Markov model within the class of maximally predictive models. Here, the corresponding so-called "-machine encodes the mechanisms of prediction. We propose an alternative notion of predictive models in terms of a hidden Markov model capable of generating the underlying stochastic process. A comparison of these two notions of prediction reveals that our approach is less restrictive and thereby allows for predictive models that are more concise than the $\varepsilon$-machine.

Feb 4, 2008
Feb 4, 2008
hidden Markov models, computational mechanics, $\varepsilon$-machines, observable operator models, prediction, epsilon-machines

Related publications

2009 Repository Open Access
Wolfgang Löhr and Nihat Ay

On the generative nature of prediction

In: Advances in complex systems, 12 (2009) 2, pp. 169-194