### Abstract

This paper develops the sufficiency principle suitable for data reduction in decentralized inference systems. Both parallel and tandem networks are studied and we focus on the cases where observations at decentralized nodes are conditionally dependent. For a parallel network, through the introduction of a hidden variable that induces conditional independence among the observations, the locally sufficient statistics, defined with respect to the hidden variable, are shown to be globally sufficient for the parameter of inference interest. For a tandem network, the notion of conditional sufficiency is introduced and the related theories and tools are developed. Finally, connections between the sufficiency principle and some distributed source coding problems are explored.

Original language | English (US) |
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Title of host publication | 2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012 |

Pages | 319-323 |

Number of pages | 5 |

DOIs | |

State | Published - Oct 22 2012 |

Event | 2012 IEEE International Symposium on Information Theory, ISIT 2012 - Cambridge, MA, United States Duration: Jul 1 2012 → Jul 6 2012 |

### Publication series

Name | IEEE International Symposium on Information Theory - Proceedings |
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### Other

Other | 2012 IEEE International Symposium on Information Theory, ISIT 2012 |
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Country | United States |

City | Cambridge, MA |

Period | 7/1/12 → 7/6/12 |

### Fingerprint

### ASJC Scopus subject areas

- Theoretical Computer Science
- Information Systems
- Modeling and Simulation
- Applied Mathematics

### Cite this

*2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012*(pp. 319-323). [6284199] (IEEE International Symposium on Information Theory - Proceedings). https://doi.org/10.1109/ISIT.2012.6284199