Optimal Nonlinear Prediction of Random Fields on Networks
Discrete mathematics & theoretical computer science, DMTCS Proceedings vol. AB, Discrete Models for Complex Systems (DMCS'03), DMTCS Proceedings vol. AB, Discrete Models for Complex Systems (DMCS'03) (2003) Cet article a éte moissonné depuis la source Episciences

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It is increasingly common to encounter time-varying random fields on networks (metabolic networks, sensor arrays, distributed computing, etc.).This paper considers the problem of optimal, nonlinear prediction of these fields, showing from an information-theoretic perspective that it is formally identical to the problem of finding minimal local sufficient statistics.I derive general properties of these statistics, show that they can be composed into global predictors, and explore their recursive estimation properties.For the special case of discrete-valued fields, I describe a convergent algorithm to identify the local predictors from empirical data, with minimal prior information about the field, and no distributional assumptions.
DOI : 10.46298/dmtcs.2310
Classification : 05A05, 05A15
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     author = {Shalizi, Cosma Rohilla},
     title = {Optimal {Nonlinear} {Prediction} of {Random} {Fields} on {Networks}},
     journal = {Discrete mathematics & theoretical computer science},
     year = {2003},
     volume = {DMTCS Proceedings vol. AB, Discrete Models for Complex Systems (DMCS'03)},
     doi = {10.46298/dmtcs.2310},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.46298/dmtcs.2310/}
}
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Shalizi, Cosma Rohilla. Optimal Nonlinear Prediction of Random Fields on Networks. Discrete mathematics & theoretical computer science, DMTCS Proceedings vol. AB, Discrete Models for Complex Systems (DMCS'03), DMTCS Proceedings vol. AB, Discrete Models for Complex Systems (DMCS'03) (2003). doi: 10.46298/dmtcs.2310

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