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- W2952220786 abstract "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." @default.
- W2952220786 created "2019-06-27" @default.
- W2952220786 creator A5082953212 @default.
- W2952220786 date "2003-05-12" @default.
- W2952220786 modified "2023-09-25" @default.
- W2952220786 title "Optimal Nonlinear Prediction of Random Fields on Networks" @default.
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