ITR/IM Bayesian Data Analysis for Digital Networked Environments

罗格斯大学像网络这样的数字网络环境在我们的生活中扮演着越来越重要的角色。超过1.35亿美国人有互联网接入(来源：eTForecast)，平均每周使用该连接4.2小时(来源：普华永道)。所有这些活动都产生了巨大而复杂的“数字痕迹”。对这些痕迹的适当分析可以导致对在线行为的前所未有的理解，进而大幅改善数字环境的设计。这些数据由于其复杂性和大规模，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。具体地说，这个项目将开发贝叶斯统计方法来分析来自数字环境的数据。数据分析的贝叶斯方法非常适合该项目关注的那种多层次和分层结构的数据。贝叶斯方法有着丰富的历史，但由于计算能力和数值方法的进步，贝叶斯方法直到最近几年才变得实用。方法论研究将以一系列应用为基础。然而，关键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巨大的教育障碍。该项目还将开发和传播一门研究生级别的贝叶斯方法多学科课程。

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.