## Discrete Models for Complex Systems, DMCS'03

### Michel Morvan and Éric Rémila (eds.)

### DMTCS Conference Volume AB (2003), pp. 11-30

author: | Cosma Rohilla Shalizi |
---|---|

title: | Optimal Nonlinear Prediction of Random Fields on Networks |

keywords: | Networks, random fields, sufficient statistics, nonlinear prediction, information theory, recursive estimation |

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. |

If your browser does not display the abstract correctly (because of the different mathematical symbols) you may look it up in the PostScript or PDF files. | |

reference: | Cosma Rohilla Shalizi (2003),
Optimal Nonlinear Prediction of Random Fields on Networks, in
Discrete Models for Complex Systems, DMCS'03, Michel
Morvan and Éric Rémila (eds.), Discrete
Mathematics and Theoretical Computer Science Proceedings
AB, pp. 11-30 |

bibtex: | For a corresponding BibTeX entry, please consider our BibTeX-file. |

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pdf-source: | dmAB0102.pdf (168 K) |

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