Bayesian Methods for Large-Scale Applications

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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