Bayesian Methods for Large-Scale Applications

摘要提案编号：DMS-0505599研究机构：罗格斯大学新玩法NSF研究所：统计学研究人员：Madigan，David提案标题：大规模应用的贝叶斯方法 研究人员致力于贝叶斯统计方法的大规模应用。三个应用程序提供了工作的背景。“文本分类”涉及到自动分配文档到预定义的类别，需要超高维的监督学习模型。“作者归属”使用类似的方法，但试图识别匿名文档的作者。“定位”问题使用信号特征在无线网络中定位用户。 研究人员专注于跨越这些应用的技术挑战，包括序贯贝叶斯分析，非线性优化和新奇检测算法。在商业和科学领域，计算的进步彻底改变了数据分析的作用。 从历史上看，分析师在本地生成数据以解决特定的研究问题。现在，无处不在的计算和廉价的存储已经将数据的产生与研究问题分离开来。各种各样的数据被产生并存储在可远程访问的数据库中，同时考虑到无数的问题，包括可预见的和不可预见的。历史上统计学一直致力于从有限的数据中挖掘出最大的信息量。研究人员的工作重点是针对这些新兴的大规模应用的所谓贝叶斯统计方法

ABSTRACT PROPOSAL NUMBER.: DMS-0505599 INSTITUTION: Rutgers University New Brunswick NSF PROGRAM: STATISTICSPRINCIPAL INVESTIGATOR: Madigan, DavidPROPOSAL TITLE: Bayesian Methods for Large-Scale Applications The investigators work on Bayesian statistical methods for large-scaleapplications. Three applications provide the backdrop for the work."Text categorization" concerns the automatic assignment of documentsto predefined categories and requires ultra-high dimensional supervisedlearning models. "Authorship attribution" uses similar methodologybut attempts to identify authors of anonymous documents.The "Localization" problem uses signal characteristics to locate users in wireless networks. The investigators focus on technical challenges that span these applications including sequential Bayesian analysis,non-linear optimization, and novelty detection algorithms.In both the business and scientific realms, computing advances havedrastically altered the role of data analysis. Historically, analystsproduced data locally to address specific research questions. Now,ubiquitous computing and cheap storage have decoupled the productionof data from the research questions. Data of all kinds are producedand deposited in remotely accessible databases with myriad questionsin mind, both foreseen and unforeseen. Statistics has historicallyfocused on squeezing the maximum amount of information out of limiteddata. The investigator's work focuses instead on so-calledBayesian statistical methods for these emerging larger-scale applications