Distances in probability space and the statistical complexity setup
Statistical complexity measures (SCM) are the composition of two ingredients: (i) entropies and (ii) distances in probability-space. In consequence, SCMs provide a simultaneous quantification of the randomness and the correlational structures present in the system under study. We address in this rev...
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| Autores principales: | , , , , |
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| Formato: | Artículo publishedVersion |
| Lenguaje: | Inglés |
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2011
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_10994300_v13_n6_p1055_Kowalski |
| Aporte de: |
| Sumario: | Statistical complexity measures (SCM) are the composition of two ingredients: (i) entropies and (ii) distances in probability-space. In consequence, SCMs provide a simultaneous quantification of the randomness and the correlational structures present in the system under study. We address in this review important topics underlying the SCM structure, viz., (a) a good choice of probability metric space and (b) how to assess the best distance-choice, which in this context is called a "disequilibrium" and is denoted with the letter Q. Q, indeed the crucial SCM ingredient, is cast in terms of an associated distance D. Since out input data consists of time-series, we also discuss the best way of extracting from the time series a probability distribution P. As an illustration, we show just how these issues affect the description of the classical limit of quantum mechanics. © 2011 by the authors; licensee MDPI, Basel, Switzerland. |
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