ITR/IM Bayesian Data Analysis for Digital Networked Environments

数字网络环境的 ITR/IM 贝叶斯数据分析

• 批准号: 0113236
• 负责人: David Madigan
• 金额: $ 24.52万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0113236&HistoricalAwards=false
• 关键词: ITR; IM; Bayesian; Data; Analysis

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.

digan - Rutgers university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（数据来源：etforecast），平均每周上网4.2小时（数据来源：普华永道）。所有这些活动都会产生巨大而复杂的“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，并反过来对数字环境的设计进行实质性的改进。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，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，但直到最近几年，由于计算能力和数值方法的进步，才变得可行。方法学研究将立足于一系列的应用。然而，最重要的贡献将是为这些数据开发一种实用的贝叶斯方法。贝叶斯方法的广泛应用存在着很大的教育障碍。该项目还将开发和传播一门研究生水平的贝叶斯方法多学科课程。