ITR/IM Bayesian Data Analysis for Digital Networked Environments

数字网络环境，如Web，在我们的生活中扮演着越来越重要的角色。超过1.35亿美国人可以上网（资料来源：eTForecasts），平均每周上网4.2小时（资料来源：PriceWaterhouseCoopers）。 所有这些活动都会产生大量复杂的"数字痕迹"。 对这些痕迹进行适当的分析可以导致对在线行为的前所未有的理解，并反过来大大改善数字环境的设计。 这些数据由于其复杂性和庞大的规模，对传统的统计方法提出了挑战。 我们将开发新的统计技术来应对这些挑战。具体来说，本项目将开发用于分析数字环境数据的贝叶斯统计方法。 贝叶斯方法的数据分析是唯一适合于这种多层次和层次结构化的数据，该项目的重点。贝叶斯方法有着丰富的历史，但只是在最近几年才成为实用的，由于在计算能力和数值方法的进步。方法论的研究将立足于一系列的应用。然而，重要的贡献将是为这些数据开发一种实用的贝叶斯方法。 贝叶斯方法的广泛使用存在着重大的教育障碍。该项目还将编制和传播贝叶斯方法研究生一级的多学科课程。

ABSTRACTDMS-0113236Madigan - Rutgers UniversityDigital networked environments such as the Web play an increasingly important role in our lives. More than 135 million Americans have Internet access (Source: eTForecasts) and on average use that connection 4.2 hours per week (Source: PriceWaterhouseCoopers). All this activity generates vast and complex "digital traces". Appropriate analyses of these traces can lead to an unprecedented understanding of online behavior, and, in turn, to substantial improvements in the design of digital environments. These data present challenges to traditional statistical methods due to their complexity and massive scale. We will develop novel statistical technology to address these challenges.Specifically, this project will develop Bayesian statistical methodology for analyzing data from digital environments. The Bayesian approach to data analysis is uniquely suited to the kind of multi-level and hierarchically structured data that the project focuses on. Bayesian methods have a rich history but have only become practicable in recent years due to advances in computing power and numerical methods. The methodological research will ground itself in a series of applications. The essential contribution, however, will be to develop a practical Bayesian methodology for these data. There exists a significant educational barrier to the widespread use of Bayesian methods. The project will also develop and disseminate a graduate-level multidiscipliniary course in Bayesian methods.