ON THE GENERATIVE NATURE OF PREDICTION

Wolfgang Lã–hr and Nihat Ay
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Wolfgang Lã–hr: Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany
Nihat Ay: Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany; Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA

Advances in Complex Systems (ACS), 2009, vol. 12, issue 02, pages 169-194

Abstract: 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 ε-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 ε-machine.

Keywords: Hidden Markov models; computational mechanics; ε-machines; prediction (search for similar items in EconPapers)
Date: 2009

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