A Unifying Framework for Distributed Inference in Networked Systems

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。目标：该项目的目标是开发一个新的框架，用于研究具有依赖观察的分布式推理。该项目将解决广泛的问题，从分布式检测、分布式估计、鲁棒推断到大型系统设置中的渐近性能。智力上的优点：智力上的优点在于所提出的框架的统一性。现有的具有依赖观测值的分布式推理结果是相当碎片化的。提出的框架统一了这些孤立的研究，并为现有文献中报道的许多观察提供了直觉。这种直觉，以及框架本身，可以应用和适应复杂系统中的各种分布式推理。所提出的研究的变革性质在于它有可能将现有的孤立研究联系起来，并识别和解决以前从未解决过的分布式推理问题，从而为现实依赖数据模型下的分布式推理系统提供明确的设计原则。广泛的影响：广泛的影响是多方面的。该研究揭示了在分布式系统中处理依赖观测困难的根本原因；它提供了有用的见解，可能会导致分布式推理以外的领域的研究进展。研究结果将通过出版物和教学报告向研究界传播，并通过课程编制向研究生传播。鼓励和促进研究生参加专业会议和会议，招募本科生参加研究项目，这将有助于培养他们对科学活动的热情和兴趣。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)Objective: The objective of this program is to develop a new framework for studying distributed inference with dependent observations. A broad range of issues will be addressed under this program, ranging from distributed detection, distributed estimation, robust inference, to asymptotic performance in a large system setting. Intellectual merit: The intellectual merit is in the unifying nature of the proposed framework. Existing results in distributed inference with dependent observations are rather fragmented. The proposed framework unifies these isolated studies and provides intuition behind many of the observations reported in existing literature. Such intuition, as well as the framework itself, can be applied and adapted to various distributed inference in complex systems. The transformative nature of the proposed research lies in its potential to connect existing isolated studies, and to identify and resolve distributed inference problems that were never before addressed, thereby providing clear design principles for distributed inference systems under realistic dependent data models.Broader impacts: The broader impacts are multifaceted. The study sheds light on the fundamental cause of difficulty in dealing with dependent observations in distributed systems; it provides useful insights that may lead to research advances in areas beyond that of distributed inference. The research results are to be disseminated to both the research community through publications and tutorial presentations and to graduate students through curriculum development. Encouraging and facilitating graduate student participation in professional meetings and conferences and recruiting undergraduate students in research projects which will help instill enthusiasm and foster their interest in scientific activities.