分散式数据约简的充分性原则
The Sufficiency Principle for Decentralized Data Reduction
摘要：
本文提出了适用于分散推理系统数据约简的充分性原理。并行和串联网络进行了研究，我们专注于在分散节点的观察是有条件依赖的情况下。对于一个并行网络，通过引入一个隐藏的变量，诱导条件独立的观察，局部充分的统计，相对于隐藏的变量定义，被证明是全球足够的参数的推断兴趣。针对串联网络，提出了条件充分性的概念，并发展了相应的理论和工具。最后，充分性原则和一些分布式信源编码问题之间的联系进行了探讨。
英文摘要：
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.
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