Bayesian Methods for Large-Scale Applications

摘要提案号。项目：statistics项目负责人：Madigan， david提议题目：大规模应用的贝叶斯统计方法研究人员致力于大规模应用的贝叶斯统计方法。三个应用程序为这项工作提供了背景。“文本分类”涉及将文档自动分配到预定义的类别，并且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”问题利用信号特征来定位无线网络中的用户。研究人员专注于跨越这些应用的技术挑战，包括顺序贝叶斯分析，非线性优化和新颖性检测算法。在商业和科学领域，计算技术的进步极大地改变了数据分析的角色。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生产与研究问题分离开来。各种各样的数据产生并存储在远程访问的数据库中，脑海中有无数的问题，既有可预见的，也有不可预见的。从历史上看，统计学一直专注于从有限的数据中挤出最大数量的信息。相反，研究者的工作集中在所谓的贝叶斯统计方法上，用于这些新兴的大规模应用

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