Predictive models and generative complexity |
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Authors: | Wolfgang L?hr |
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Institution: | Wolfgang LHR Max Planck Institute for Mathematics in the Sciences,Inselstraβe 22,D-04103 Leipzig,Germany |
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Abstract: | The causal states of computational mechanics define the minimal sufficient memory for a given discrete stationary stochastic
process. Their entropy is an important complexity measure called statistical complexity (or true measure complexity). They
induce the ɛ-machine, which is a hidden Markov model (HMM) generating the process. But it is not the minimal one, although generative
HMMs also have a natural predictive interpretation. This paper gives a mathematical proof of the idea that the ɛ-machine is the minimal HMM with an additional (partial) determinism condition. Minimal internal state entropy of a generative
HMM is in analogy to statistical complexity called generative complexity. This paper also shows that generative complexity
depends on the process in a nice way. It is, as a function of the process, lower semi-continuous (w.r.t. weak-* topology),
concave, and behaves nice under ergodic decomposition of the process. |
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Keywords: | |
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