CAREER: New Information Processing Techniques from Statistical Physics and Probability Theory

职业：统计物理学和概率论的新信息处理技术

• 批准号: 0743978
• 负责人: Andrea Montanari
• 金额: $ 32万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0743978&HistoricalAwards=false
• 关键词: CAREER; New; Information; Processing; Techniques

Statistical mechanics aims at understanding the collective behavior of large systems made of simple interacting components. The space of possible configurations of such systems is high-dimensional and combinatorial and, as a consequence, it is extremely challenging to understand their behavior. Despite this, statistical mechanics techniques often provide strong quantitative predictions.A number of modern information technologies share the same complex features, in that they are based on large decentralized networks of simple units. Examples include sensor networks, ad hoc wireless networks, social communities, modern coding systems. Over the last 20 years, spectacular progress has been made in probability theory towards a rigorization of the methods of statistical mechanics. These achievements can have far-reaching consequences in the analysis and design of computer and communication systems.The present proposal has two goals: (I) Clarify and rigorize the statistical physics techniques and their applications to engineering and computer science. (II) Use this approach in three new application domains: learning graphical models; detection and measurement through sparse graphs; statistical inference for high-dimensional combinatorial distributions.

统计力学旨在理解由简单相互作用的组件组成的大型系统的集体行为。 此类系统的可能配置空间是高维和组合的，因此，理解它们的行为极具挑战性。 尽管如此，统计力学技术常常提供强有力的定量预测。许多现代信息技术具有相同的复杂特征，因为它们都基于简单单元的大型分散网络。示例包括传感器网络、自组织无线网络、社交社区、现代编码系统。 在过去的 20 年里，概率论在统计力学方法的严格化方面取得了惊人的进展。 这些成就可以对计算机和通信系统的分析和设计产生深远的影响。本提案有两个目标：（I）澄清和严格统计物理技术及其在工程和计算机科学中的应用。 (II) 在三个新的应用领域中使用这种方法：学习图形模型；通过稀疏图进行检测和测量；高维组合分布的统计推断。